heathrow airport’s cost of capital a report on behalf of...

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Heathrow Airport’s

Cost of Capital

A report on behalf of Heathrow

Public Version

February 2013

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Contents

1 Introduction .................................................................................................................................................................... 1

1.1 Estimate of Heathrow’s Cost of Capital ......................................................................................................... 1

1.2 Structure of Report .............................................................................................................................................. 1

2 Methodological Issues .................................................................................................................................................. 3

2.1 The CAPM Framework and other Cross-checks ......................................................................................... 3

2.2 Lack of Direct Market Data ............................................................................................................................... 5

2.3 Choice of the Relevant Capital Market ........................................................................................................... 7

2.4 The Impact of the Financial Crisis ..................................................................................................................... 7

2.5 Time Period ............................................................................................................................................................ 8

2.6 Gearing .................................................................................................................................................................... 9

2.7 Assumed Debt Beta ........................................................................................................................................... 10

2.8 Assumed Tax Rate .............................................................................................................................................. 11

3 Total Market Returns ................................................................................................................................................. 12

3.1 The Relationship between Market Returns and Macroeconomic Conditions ..................................... 12

3.2 The Sustainable Growth Rate (and hence Risk-Free Rate) is Likely to Increase during the Price

Control Period ................................................................................................................................................................. 18

3.3 Why the Sustainable Growth Rate is Likely to Increase ........................................................................... 21

3.4 The Equity Risk Premium .................................................................................................................................. 26

3.5 Conclusion on Total Market Returns ............................................................................................................ 32

3.6 Skewness and Non-Diversifiable Skewness in Total Market Returns .................................................... 33

3.7 Appendix: Technical Details Underpinning the Model .............................................................................. 34

4 Debt Premium ............................................................................................................................................................. 40

4.1 Introduction ......................................................................................................................................................... 40

4.2 Bond Spread Analysis ......................................................................................................................................... 40

4.3 Issuance costs ...................................................................................................................................................... 46

4.4 CEPA’s debt premium estimate ...................................................................................................................... 47

4.5 Conclusion on Heathrow’s debt premium ................................................................................................... 49

5 Developments at Heathrow and in the Airport Sector Since 2007 ............................................................... 50

5.1 Introduction ......................................................................................................................................................... 50

5.2 Macroeconomic context ................................................................................................................................... 50

5.3 Changes in demand ............................................................................................................................................ 50

5.4 Regulatory context ............................................................................................................................................. 52

5.5 The Impact of Capacity Constraints and Regulation on Skewness......................................................... 52

6 Equity Beta .................................................................................................................................................................... 53

6.1 Comparator Data ............................................................................................................................................... 53

6.2 Fundamental Beta Analysis ............................................................................................................................... 59

6.3 Skewness Analysis ............................................................................................................................................... 64

6.4 CEPA’s estimate of Equity Beta ....................................................................................................................... 75

6.5 Overall Conclusion on Equity Beta ................................................................................................................ 78

7 Overall WACC ............................................................................................................................................................ 79

7.1 Overall WACC Estimate .................................................................................................................................. 79

7.2 CEPA’s estimate of the WACC ...................................................................................................................... 81

7.3 Aiming Up ............................................................................................................................................................. 82

7.4 Conclusion ............................................................................................................................................................ 83

Appendix: Approach to Calculating Betas ...................................................................................................................... 84

Introduction

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1 Introduction

1.1 Estimate of Heathrow’s Cost of Capital

This report considers what cost of capital would be appropriate for Heathrow in the Q6 airports

price control. It has been commissioned by Heathrow Airport from Europe Economics, with a view

to ensuring that the cost of capital determined for Heathrow is no lower than is justified by market

and economic conditions and outlook.

In the Q5 control, the values for key parameters of the cost of capital determination, as proposed by

the Competition Commission and the CAA, were as shown in columns two and three of Table 1.1.

Column four contains the derivation of this report’s proposed minimum value of the WACC for Q6,

which we would currently expect to revise upwards in later phases.

Table 1.1: Overall WACC

Q5 determination

WACC estimate for Q6

from this report

Low High

Risk free rate (%)

2.5 2.5 2.0

Equity risk premium (%)

2.5 4.5 5.0

Debt Premium including

issuance cost (%) 1.05 1.05 2.6

Equity beta

0.91 1.15 1.3

Cost of equity

(post-tax) (%) 7.3 8.5

Tax rate* (%)

28 21

Cost of equity (pre-tax) (%)

10.2 10.8

Cost of debt (pre-tax) (%)

3.55 4.6

Gearing (%)

60 60

WACC (vanilla) (%)

WACC (pre-tax) (%)

6.2 7.1

The Q6 price control is, at the time of writing, expected to apply for the period April 2014 to March

2019. Even the commencement of this period is some distance away at the time of writing, in highly

volatile market conditions. We anticipate that our proposed values could be amended significantly

(probably upwards) between the time of this report and later submissions.

1.2 Structure of Report

Subsequent sections of this report proceed as follows:

Introduction

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In Section 2 we set out a number of the key methodological issues arising in respect of Q6.

Section 3 considers Total Market Returns, including key generic parameters of the CAPM model

such as the risk-free rate and the equity risk premium. The central issue of this section is how

total market returns should be expected to evolve between the time of writing and the mid-point

of the Q6 price control period.

Section 4 considers Heathrow’s cost of debt, in particular noting the rise in debt premia since the

lows of 2005-7 (now commonly acknowledged to reflect market under-pricing of risk).

Section 5 considers developments in the airports sector since the time of the Q5 Competition

Commission advice and CAA determination.

Section 6 estimates equity beta from comparator and Heathrow data.

Section 7 draws together the analysis of previous sections into an estimate for the overall

WACC, and considers the appropriate methodology for aiming up.

Methodological Issues

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2 Methodological Issues

These sections sets out the key methodological issues that we consider particularly relevant in the

context of the upcoming price review. These are:

The general framework to be used and additional cross-checks.

Issues related to the lack of direct market data.

The choice of the relevant capital market.

The impact of the financial crisis.

The relevant time period.

The assumed gearing.

2.1 The CAPM Framework and other Cross-checks

The standard way UK regulators assess the cost of capital is through the Weighted Average Cost of

Capital / Capital Asset Pricing Model (WACC-CAPM) approach. The relationship between the

CAPM and the WACC approach is discussed below.

2.1.1 The CAPM

The CAPM framework was developed in the 1960s, building on the portfolio analysis work of

Markowitz (1958), as a way to estimate the value of assets. The key feature of CAPM is that, given

its important assumptions concerning the efficiency of financial markets and that investors care only

about the mean and variance of returns, investment returns can be expressed as:

r = rf + MRP × βA [Eq. 2.1]

where r is the (expected) return on the asset, rf is the return that would be required for a perfectly

risk-free asset, MRP is the “market risk premium”, that is to say the excess return over the risk-free

rate that would be delivered by a notional perfectly diversified portfolio equivalent consisting of all

assets (“the whole market”), and βA is a measure of the correlation between movements in the value

of the asset of interest and in the value of assets as a whole. It is also called “beta” (or sometimes

the “asset beta”).

2.1.2 The vanilla WACC and the capital structure

Utilities typically use a combination of debt and equity. Therefore, in the (vanilla) WACC

framework assets’ returns are decomposed into a cost of equity and a cost of debt, according to the

following formula:

r = (E/V) × re + (D/V) × rd [Eq. 2.2]

where V is the total value of the assets of the company, E is the value of the equity, D is the value of

the debt so V ≡ D + E (D/V is the proportion of the total value of the company accounted for by

debt and is often referred to as the company’s “leverage” or “gearing” — clearly, D/V + E/V = 1), re


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is the (expected) return on equity and rd is the (expected) return on debt (note that this is not

identical to the coupon rate or yield on debt, since these do not embody probabilities of and losses

on default).

2.1.3 Equivalence between CAPM and WACC

The fundamental relationship between CAPM and WACC stems from the Modigliani-Miller (MM)

insight that the asset beta, and hence the vanilla WACC, will not change due to gearing, unless either

a) the debt tax shield has value or b) the market’s assessment of the systematic risk exposure of the

underlying cash flows has changed with gearing. Using the second Modigliani-Miller Theorem1, the

following equivalence between asset beta and vanilla WACC can be shown:

Table 2.1: Asset beta — vanilla WACC equivalence

Return via asset beta Return via vanilla WACC

MRPrr Af

DE r

ED

Dr

ED

Er

MRPED

D

ED

Err DEf .

)()( MRPrED

DMRPr

ED

Er DfEf

MRPED

DMRP

ED

Err DEf

MRPED

DMRP

ED

Err DEf

The above utilises the decomposition of the corporate debt premium into debt beta and market risk premium components

In Table 2.1 βe is a measure of the correlation between movements in the value of the company’s

equity and in the value of assets as a whole. Similarly, βd is a measure of the correlation between

movements in the value of the company’s debt and in the value of assets as a whole. Since returns

on equity and debt tend to move under different circumstances (e.g. when companies are not in

default debt returns differ little but equity returns may differ considerably, whilst when companies

are in default equity returns differ little (they are close to zero) whilst debt returns differ markedly

(depending on the scale of the default)), equity and debt betas are not typically the same.

2.1.4 Alternatives to CAPM

In some other price reviews, regulators have considered alternatives to CAPM, typically to act as

cross-checks on the main CAPM calculation. Some of the alternatives to CAPM that have been

considered in other contexts would not appear to be relevant for Heathrow at Q6. In particular:

The Fama French model is typically used to take account of small company effects, which would

not be relevant to Heathrow airport. Moreover, the use of the Fama and French approach has

been heavily criticised by the CC in the previous (Q5) review.

The Dividend Growth Model (DGM) can be used to estimate the cost of equity, but the CC has

noted that it is compatible with a very wide range estimate, thus limiting its practical usefulness.

Moreover, the CC was highly sceptical about the value of the DGM in the context of the Bristol

Water judgment, stating that they “regard the DGM evidence as consistent with a wide range of

figures for the cost of WaSCs’ equity”.2

1 The Modigliani-Miller theorem implies the following expression:

ED

D

ED

EDEA .

2 http://www.competition-commission.org.uk/rep_pub/reports/2010/fulltext/558_appendices.pdf


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In the Q5 price control, the CAA noted the potential value, at least as a cross-check, of the third

moment CAPM model. The third moment CAPM addresses the possibility that investors have

preferences over the distribution of returns that go beyond mean and variance, in particular taking

account of skew. The potential usefulness of the third moment CAPM can be justified on the

following grounds:

The third moment CAPM represents a natural extension of the traditional CAPM framework (as

opposed to being a completely separate methodological approach).

The third moment CAPM has been mentioned by the CC as a potentially relevant cross-check

method for estimating the cost of capital in the airport sector, and it has not been the subject of

heavy criticism (as is the case for the DGM and Fama and French approaches).

Airports’ returns in general, and Heathrow’s returns in particular, are likely to be negatively

skewed (mainly due to the joint effects of capacity and regulatory constraints, a point which is

discussed in more detail below) which makes consideration of the results of the third moment

CAPM model particularly relevant.

Market to Asset Ratios (MARs) for listed companies are typically used as one source of evidence on

whether the regulated WACC is higher or lower than the true market WACC (although one needs

to bear in mind that other factors may also affect MARs). Regulators may also look at MARs

following draft determinations to see whether the overall price control package seems too harsh or

too generous.

2.2 Lack of Direct Market Data

The most significant methodological challenge in the upcoming price control review is the lack of

direct market data for Heathrow Airport Holdings. (In the Q5 review direct market data for

Heathrow was lacking, but market data for the then BAA group was available up until only a few

months before the CAA’s initial estimates were produced). We therefore list below potential

approaches to deal with this issue.

2.2.1 Use of comparators

When direct market data for the entity of interest is lacking it is common practice to infer the asset

beta of the entity of interest (Heathrow airport in this case) based on analysis of relevant

comparators for which market data is available. In the last price control review a number of

international airports, airline companies and UK utilities were used as comparators. We shall

discuss below which potential comparators are most relevant to use on this occasion.

Beta estimates can vary significantly across comparators, and not all comparators are equally

representative of Heathrow’s exposure to systematic risk. A degree of judgment is therefore

needed in deciding to which estimates more weight should be attached. A number of criteria can be

used to assist this process. For example, airports with significant similarities to Heathrow (e.g. in

passenger numbers, status as an international hub, full capacity utilisation etc.) could be considered

to be more representative than UK water companies. Nevertheless, inferring Heathrow’s asset beta

directly from comparators’ estimates alone can be problematic and would be necessarily subject to a

material degree of arbitrariness.


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2.2.2 Changes since the last period in which direct market data was available

Another potential use of comparators is to assess changes occurring since the last price control

period. This approach does not attempt to assess a precise beta estimate for Heathrow, but rather

indicates the direction of a change (e.g. whether Heathrow’s asset beta has increased or decreased

since the Q5 price control). In the context of the third moment CAPM, a particular consideration

would be whether there is evidence that the skewness of returns to Heathrow has changed since

the time of the last price review.

2.2.3 Fundamental and accounting beta

Alongside the comparator beta analysis, another source of evidence on Heathrow’s asset and equity

betas would be the employment of account and fundamental beta methodologies.

Accounting betas are based on econometric analysis of accounting returns rather than stock market

returns, and hence can be calculated even for non-listed companies. In particular, it would be

possible to regress changes in accounting earnings for Heathrow airport against changes in earnings

for an equity index (such as the FTSE All Share).

However, there are various potential limitations with this approach. One is that firms do not publish

measured earnings on a daily basis, leading to regressions with fewer observations and limited

statistical power compared to those obtainable with daily market data. Another is that earnings are

often smoothed out and subject to accounting judgments, leading to potential mis-measurement of

accounting betas. Finally, accounting information on earnings reflect ex post achieved earnings,

whereas stock prices used in normal beta estimation reflect expectations of future earnings.

A more sophisticated variant of this approach is to calculate what are called “fundamental” betas.

This involves estimating a relationship between various fundamental characteristics of firms and their

betas, and then use the characteristics for Heathrow airport to estimate Heathrow’s beta.

However, it should be noted that a fundamental beta analysis will only imperfectly account for the

effects of systematic skewness, especially insofar as a company has skewness disproportionate to its

variance (as we shall argue Heathrow does).

2.2.4 Disaggregation of Ferrovial’s beta

The largest shareholder in Heathrow airport is Ferrovial, until recently with 33.65 per cent of its

equity. Heathrow is a material component of Ferrovial’s overall business. As at 20103:

the airport business accounted for approximately 23 per cent of Ferrovial’s total revenues, 50

per cent of Ferrovial’s earnings, and 50 per cent of Ferrovial’s total assets;

Heathrow was responsible for 70 per cent of the revenues and 75 per cent of earnings generated

by Ferrovial in its airport business segment, and hence 16 per cent of total revenues and 38 per

cent of earnings.

When a regulated entity is a material part of a broader non-regulated business, regulators

sometimes choose to infer the beta for the regulated entity by disaggregating the beta for the overall

entity. This is, for example, the approach that has been taken by Ofcom to the regulation of BT.

However, such exercises are most normally conducted when the target entity (in this case

Heathrow) constitutes a majority of the total business. A 16 per cent share in revenues / 38 per

3 These figures were sourced from Ferrovial’s annual accounts (January-December 2010)


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cent in earnings, though significant, is unlikely to be sufficient for such an exercise to constitute the

main data basis for a regulatory decision.

2.3 Choice of the Relevant Capital Market

Past regulatory decisions concerning utilities in the UK have adopted the UK as the relevant capital

market. For instance, beta analyses based on European and world market indices have been used

mainly as cross-checks but have never represented the centrepiece of the analysis. Obviously an

airport has, intrinsic to its business, a straightforwardly international dimension that is of a

completely different order from any international dimension in, say, a water company. Furthermore,

as we understand it, the typical investor relevant to Heathrow operates mainly in developed western

economies.

It might therefore be appropriate to place more weight, at least for some parts of the estimation

exercise, on European and US figures as opposed to UK figures. Alternatively, even if the UK

market continued to be the main source of data, a greater weight might be placed upon international

data than would be the case for UK utilities.

2.4 The Impact of the Financial Crisis

The financial crisis had a significant impact on cost of capital parameters. There have been a number

of key regulatory decisions regarding the cost of capital since 2008, including Ofwat (2009), Ofcom

(2009, 2011), Ofgem (2011) and the Competition Commission’s judgement on the Bristol Water

case (2010). Based on these judgments, the following trends can be noted:

Increase in Equity Risk Premium (ERP) — periods of high market turbulence are associated with

an increased perceived risk of equity investments and, as a result, investors tend to require

higher premiums. ERP regulatory judgements have tended to rise, from typical figures of around

2.50 - 4.50 per cent in 2008 (as per the Q5 Competition Commission recommendation). During

the height of the credit crisis, figures above 5 per cent were used (e.g. 5.25 per cent for

Electricity Distribution in 2009, 5.4 per cent for Water in 2009). Some recent figures have fallen

back to around 5 per cent (e.g. 4.0-5.0 per cent in the Bristol Water judgement, and 5.0 per cent

in Ofcom’s OpenReach judgement of 2011), though in its final proposals for electricity and gas

transmission and gas transmission in 2012, Ofgem proposed an ERP of 5.25 per cent

Decrease in risk-free rate — as the economic conditions deteriorate market expectations about

the economy’s sustainable growth are revised downward and the risk-free rate falls. In fact, risk-

free rate judgements have tended to fall over time, from figures such as the CAA’s Q5 judgement

of 2.50 per cent in 2008, to the 1-2 per cent range of the Competition Commission’s 2010

Bristol Water judgement and the 1.4 per cent figure in the Ofcom OpenReach judgement of

2011. In its final proposals for electricity and gas transmission and gas transmission in 2012,

Ofgem proposed a risk-free rate of 2.0 per cent.

Increase in debt premium — Bond spreads rose sharply during the financial crisis, but have since

come down — though without falling back to their pre-crisis lows. A crucial issue is what view

to take about where bond spreads are likely to settle during the forthcoming price control

period. For instance, it is arguably not appropriate to assume that spreads will end up around the

level seen immediately prior to the financial crisis, since it can be argued that risk was being

under-priced during this period.


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Other potential impacts — In addition to the impacts illustrated above, which can be viewed —

to a certain extent — as being transitory, an interesting issue is whether the behaviour of

financial markets during the recent global recession has changed the shape of the distribution of

expected investment returns. The recent global recession might demonstrate that perceived

downside risk in some sectors is now greater than previously thought, tending to balance out

returns that were previously perceived as skewed upwards. For example, perhaps in the

regulated sector some investors previously thought that the worst that regulated entities might

do would be to secure their cost of capital, but that there was potential for them to outperform

regulatory assumptions and so gain on the upside; whereas now it is better understood that

there is genuine downside risk. Or alternatively, some stakeholder might argue that for many

assets, there were previously fairly balanced returns but there is now seen to be downside tail

risk, skewing distributions.

Assuming one does proceed with a UK-based WACC analysis, another issue is the relevance of

“crisis” analysis of the cost of capital to the current review. The Q5 determination was undertaken

at the beginnings of the credit crisis. Determinations by regulators since then have had to take

account of the financial crisis and market turbulence. This has had implications for inferring the cost

of capital from historic or contemporaneous data. A key is question is therefore: is the crisis over

or will it be over by the time of the next price control period?

For the purposes of the main estimates reported here we shall assume that by the time the Q6 price

control commences, the acute phase of the crisis will be over. However, this is clearly a question

that is material and should be kept under close review. Alongside our main estimates, we report in

specific sections below the implications for key affected parameters if the crisis were to persist.

However, our overall estimates in this document do not assume a crisis scenario.

2.5 Time Period

The Q6 price control is, at the time of writing, expected to apply for the period April 2014 to March

2019. The precise appropriate weightings, across the price control period, for cost of capital

purposes might reflect a number of complex factors, such as the scheduling of the capital

programme for Q6, that are not available to us at the time of writing. We shall assume, for our

purposes here, that the desired exercise is to estimate what the cost of capital for Heathrow will be

at the mid-point of the price control period — roughly at the turn of the year 2016/17 or, more

strictly, October 2016.

As we write, in the first half of 2012, October 2016 is clearly some distance away, and much could

happen. Very often in finance, the values of contemporaneous forwards-looking variables constitute

the best-estimate of future variables — even, sometimes, quite distant variables. Even when market

data allow us to infer implied estimates for how views about forwards-looking variables will change

in the future, relatively modest differences in the implied future values and the actual current value

are often best explained in terms of factors such as liquidity, uncertainty, and transactions costs.

In current and recent very extreme market conditions, however, (i) it seems much less likely than

usual that current forwards-looking estimates provide a best-estimate of circumstances more than

four years ahead — fundamental analysis of economic conditions and outlook might provide us with

strong reasons to believe that conditions in more than four years’ time will be very different from

those today; and (ii) as we shall see below, implied estimates, drawn from current market data, for

how views about forwards-looking variables will change in the future involve changes far greater in

magnitude than those normally attributed to liquidity, uncertainty or transactions costs.


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There are thus strong reasons to believe that key elements of the cost of capital in more than four

years’ time could be different from that indicated by market data today.

2.6 Gearing

The gearing assumed for Heathrow in the Q5 price control was 60 per cent. For Q5, Heathrow’s

notional gearing was determined at 60 per cent. In our August 2012 report we took as given the

assumption that Heathrow’s notional gearing would be 60 per cent in Q6, also. We consider what

are the implications of recent trends in gearing for the continued appropriateness of this 60 per cent

assumption. The most widely-quoted analysis of UK non-financial sector indebtedness is that from

the McKinsey Global Institute. In the chart below we provide evidence from the latest available

report at the time of writing (the January 2012 report4).

Figure 2.1: McKinsey Global Institute January 2012

Source: McKinsey Global Institute

We see first that in the period leading up to the Q5 determination non-financial sector indebtedness

rose, relative to GDP, as indeed it had been rising for much of the previous two decades. However,

whilst aggregate UK indebtedness has increased since the time of the Q5 determination in 2008,

driven particularly by increases in bank and government debt, the non-financial corporate sector has

been deleveraging, down from 122 per cent of GDP to 109 per cent.

4 http://www.mckinsey.com/insights/mgi/research/financial_markets/uneven_progress_on_the_path_to_growth


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If the Q5 determination’s choice of a notional 60 per cent gearing reflected an assumption that the

trends of the previous two decades would be extended, such that 60 per cent gearing was

“forwards-looking”, then the natural assumption might be that the Q5 notional gearing of 60 per

cent was likely to have been too high — the trend of rising gearing did not continue. In that case it

might be natural to consider how much gearing should fall in Q6.

However, we note that the Q5 determination did not aspire fully to reflect then-recent trends of

increased leverage; neither did it anticipate further leverage rises. Notional gearing was raised,

recognising the increase in gearing associated with the January 2006 takeover, but not as far as actual

gearing. We observe that the Q4 notional gearing was 25 per cent so the increase to 60 per cent

already takes account of the gearing impact of the Ferrovial takeover.

This conscious decision to under-shoot those then-recent trends in infrastructure and utilities

leverage can be seen as having been vindicated by subsequent market developments. The question,

then, is not whether Q5 gearing trends have suggested that the Q5-determined gearing was errant

— quite the reverse is true. Rather, the question is whether recent trends imply there is a secure

basis for believing that Q6 gearing will be either materially higher or lower than that in Q5.

At this stage we see no such basis. There is expected to be significant deleveraging in the UK, but

this is expected to occur principally in the household and banking sectors. The non-financial

corporate sector has already deleveraged whilst leverage in the banking sector continued to rise. It

is possible that the non-financial corporate sector will deleverage further yet, in response to wider-

economy deleveraging. However, it is also possible that economic recovery will be associated with

an increased appetite for debt amongst non-financial corporates.

At this stage, we see no compelling reason to deviate from the 60 per cent notional gearing

assumption used in Q5, and would propose that analysis proceeds on that basis pending any basis for

a change.

2.7 Assumed Debt Beta

The debt beta assumed for Heathrow in the Q5 price control was 0.1. The reasons for choosing a

positive debt beta rather than the zero chosen in regulatory determinations up to that point

reflected the special circumstances of that review, as set out on p10 paragraph 3.4 of the “CAA’s price

control reference for Heathrow and Gatwick airports, 2008-2013 — Supporting Paper II” (March 2007)5,

namely:

Large step change in gearing;

Absence of observable equity data after the step change in gearing;

Relatively low equity beta before the change in gearing; and

Relatively high debt beta.

Since BAA/Heathrow Airport Holdings equity beta data has not been available since 2006, we

assume that the issue of there being a large step change in gearing shortly after the delisting of the

regulated entity’s equity does not arise in this review.

It was noted in the Q5 review, and has been noted in subsequent reviews such as Ofwat (2008-9)

and Ofgem (2010-11) that where these special factors do not arise the assumption of a zero debt

5 http://www.caa.co.uk/docs/5/ergdocs/ccref_sp2.pdf


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beta is usually adequate for calculation purposes, as having a variable debt beta does not have a

material impact on the result. In these reviews debt betas of 0 or 0.1 were used.

In price reviews where use of a debt beta was investigated in more detail, as in Ofcom’s July 2011

WBA determination, its use was in specific response to particular methodological issues associated

with asset beta instability during 2009 under a variable gearing but non-variable debt beta estimation

model. Since we assume an invariant nominal gearing in this case, those gearing instability issues do

not arise here.

In the case of the methodologies discussed here, in addition to the above points, the implications of

a positive debt beta are not unambiguous, in that in some cases (e.g. when using Ferrovial data in our

fundamental beta analysis) the assumed notional Heathrow gearing (60 per cent) is below that of the

model (we “relever down” — in which case a higher debt beta would produce a higher asset beta),

whilst in other cases (e.g. when using comparator data from other airports such as Fraport) the

notional Heathrow gearing is higher (we “relever up”).

The lack of unambiguous directionality to the impact of debt beta undermines the case for using

non-zero estimates of it, given the intrinsic uncertainties in, and lack of a consensus method for, its

estimation. Calculating an elaborate debt beta on this occasion adds computational complexity and

estimation uncertainty without unambiguous significance for the final answer.

Bearing in mind the points above, for the purposes of this present report we assume that the

determined debt beta will be either 0 or 0.1.

2.8 Assumed Tax Rate

The tax rate assumed for Heathrow in the Q5 price control was the statutory tax rate of 28 per

cent. For the purposes of this present report we simply assume that the tax rate in Q6 will again be

the statutory tax rate. As per the most recent Treasury announcements, this will be 21 per cent.

Total Market Returns

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3 Total Market Returns

In this section we shall argue the following points:

When the economy does better, total enterprise returns are greater (and vice versa).

This tends to mean that, when the economic outlook is better (i.e. the economy is expected to do

better in the future), required total market returns to capital also tend to be higher (and vice

versa).

Matters can, however, be somewhat complicated by the fact that total enterprise returns are

divided between returns to capital and returns to labour. Evidence suggests that labour may be

obtaining a diminishing share of total returns.

There is a relationship between the risk-free rate of return and the sustainable growth rate of the

economy, both in theory and in statistical evidence.

There is good reason to believe that, although the next few years may see quite low growth for

the UK economy (indeed, perhaps the economies of many developed countries), within the next

few years the medium-term outlook (the outlook beyond the next few years) may improve,

raising sustainable growth rates and associated with a rise in the risk-free rate.

When economic conditions are weak, the equity risk premium tends to be elevated. However,

the elevation in the equity risk premium is not always as great as the fall in the risk-free rate, so

total market returns often fall.

Conversely, when economic conditions improve, although the equity risk premium may fall back,

it should not be expected to fall back as much as the risk-free rate rises, so total market returns

should be expected to rise.

After a major economic and financial crisis, one might expect lasting impacts on risk appetites.

A major economic and financial crisis might also be associated with changes in (a) the degree of

skewness and kurtosis in returns; and (b) how much investors care about skewness and kurtosis

(e.g. the price of skewness).

3.1 The Relationship between Market Returns and Macroeconomic

Conditions

3.1.1 Impact of the economy on total returns to enterprise

When economic growth is higher, firms tend to have greater earnings. Demand is higher, so the

gross value added by businesses increases. Faster economic growth leads to greater total enterprise

returns.

So, if economic growth is expected to be higher in the future, there are expected to be greater

enterprise returns. Total enterprise returns are divided between labour and capital. If the split (the

ratio) can be taken as given (or indeed if returns to labour can be taken as fixed), then a rosier

economic outlook implies that returns to capital will be greater. If investors, responding to a rosier

economic outlook, did not demand higher returns, they would be conceding that labour would take


- 13 -

all the benefit from faster growth. Normally, however, capital demands its share of the expected

larger pie.

This is the straightforward case, but it is worth noting that there is no iron rule here. If there is a

change in the capital/labour split of returns, that could in principle reverse the overall effect or

enhance it. For example, poor economic times could coincide with a fall in the share of total returns

taken by labour, so that total returns to capital could rise even as total enterprise returns fell — in

which case our straightforward case effect would be reversed. As an alternative example, rosier

economic times could coincide with labour taking a lower share of total returns — so our

straightforward case effect would be enhanced.

As it happens, evidence suggests that labour has obtained a very stable share of total returns over

the past decade — employee compensation was 54.5 per cent of GDP in 2000 and 54.8 per cent of

GDP in 2010.6 The key change here occurred during the 1980s. In 1970 and 1980 employee

compensation was around 59 per cent of GDP, but by 1990 this had fallen to 55 per cent. Since

1990 the proportion has been very stable.

If a period of elevated returns is relatively brief — for example, if it occurs only for a year or two in

the recovery phase from a recession — then although actual returns to capital may be higher, the

required rate of return will not. Over the lifetime of an investment, there will naturally be some

years in which actual rates of return are below the cost of capital and others in which actual rates of

return are higher. But overall, average expected rates of return will equal the cost of capital.

On the other hand, periods of slower or higher growth could be more sustained than this. In

economics, the “long-term” refers to the period over which there are no fixed costs — when all

investments must be renewed. A period of low or high growth sustained for a longer period than

the lifetime of investments is not merely cyclical in nature; it is structural, and should be expected to

affect not merely year-to-year actual returns but also the required rate of return on investment,

because if low / high growth is sustained and economy-wide, then it affects the opportunity cost of

investment; we can neither invest in something else nor can we simply wait a brief time and invest

under more favourable circumstances. Some of the higher returns effect will appear in the value of

assets, as opposed to the required rate of return on assets (e.g. assets will have lower prices when

outlooks are worse). But if the rate of growth in returns is systematically higher (i.e. returns are not

simply higher but grow at a faster rate each year), then required rates of return will be higher as well

as prices being higher.

Lastly, we observe that economic “shocks” affecting the sustainable growth rate can be both good

and bad in nature. There might be new technologies that raise the sustainable growth rate (e.g. by

stimulating more rapid innovation); there might be periods of sustained bad weather damaging

harvests (e.g. for a couple of decades).

Thus, if (as we shall argue below), within the next few years the long-term (i.e. longer than the

investment life) economic outlook will improve, we should expect total required rates of return to

capital to increase. To reiterate, note carefully: to deliver this result we are not required to argue

that economic conditions will improve over the next few years. Contemporaneous economic

conditions affect required returns only insofar as they provide evidence of expected future economic

conditions. Our case will thus not be that the economy should be expected to improve over the

next few years; rather, it will be that within the next few years the medium-term outlook will

6 Source National Statistics, UK Economic Accounts, Table A3: Gross domestic product: by category of

income


- 14 -

improve (in particular, for the average growth rate over the ten to twenty years from the

commencement of the price control period).

3.1.2 Relationship between the sustainable growth rate and the risk-free rate

It is common to think of the risk-free rate of return as an exogenous taste variable — if not actually

constant, then at least fixed by factors outside portfolio decision-making. We think of the risk-free

rate as a measure of impatience, of how much we would rather have things today than tomorrow.

However, though there is much in this, it is not quite the whole story. For the risk-free rate is not

simply the return any one individual would require to hold a risk-free asset. Rather, it is the return

that would be available in such an asset. As such, (a) it reflects collective tastes, rather than those of

any individual — the “taste” of the Market; and (b) it reflects an (albeit notional) equilibrium

condition.

In standard long-term economic growth models, such as the Ramsey-Cass-Koopmans model, a key

equilibrium condition is that (absent population growth) the sustainable growth rate of the economy

equals the risk-free rate.7 Indeed, in corporate finance theory the risk-free rate of return is

sometimes viewed as arising from the sustainable growth rate (i.e. causality runs from the sustainable

growth rate to the risk-free rate).

For our purposes here, we need not fully endorse either of these positions. Instead, we make the

more limited claim that one should expect changes in the risk-free rate to be correlated with

changes in the sustainable growth-rate.

We can make this thought more concrete by considering the likely relationship between the

sustainable growth-rate and our best proxy for the risk-free rate, namely yields on government

bonds. If, for example, yields on medium- to long-term government bonds are very low, we should

interpret that as an indicator that the sustainable growth rate of the economy is expected to be very

low. To see why, consider an investor that is willing to buy a government bond at a very low yield.

That investor is choosing to purchase that government bond in preference to, for example, shares

or bonds in any other business in the real economy. But that must indicate that expected returns

for the real assets of these other real economy businesses are expected to be low or very volatile.

Let us set aside the high volatility case for now, and focus on the case in which returns of these real

economy businesses are low. If returns to all real assets are low, over the medium- to long-term,

then the economy can only be expected to grow slowly over the medium- to long-term. But the

sustainable growth rate is simply the rate at which the economy can grow over the medium- to

long-term. So (setting aside issues of policy mistakes etc. that might eventually be rectified), when

government bond yields are very low, one plausible explanation is that the sustainable growth rate of

the economy is expected to be very low.

Consider the following graph.

7 Ramsey, F.P. (1928), "A mathematical theory of saving", Economic Journal, 38, 152, pp543–559. Cass, D.

(1965), “Optimum Growth in an Aggregative Model of Capital Accumulation”, Review of Economic Studies,

37 (3), pp233–240.


- 15 -

Figure 3.1: Comparison of normalised GDP series with quarterly growth (1985Q1 = 100)

Source: Europe Economics calculations

In this graph we compare the average quarterly yield on ten-year index-linked bonds (in blue) with

the actual average growth rate over the subsequent ten years (in red). To make the relationship

easier to see, we have “normalised” both series so that, as they begin in the first quarter of 1985, we

call them both 100. Because they look ahead ten years, the data in this graph ends at the beginning of

2001 (we’ll look ahead below). We can see that movements in the red graph mirror movements in

the blue graph fairly well, though not perfectly. (The correlation between the red and blue graphs is

0.49, which is certainly respectable.) If we believe that the introduction of inflation targeting in the

fourth quarter of 1992 can be treated as a game-changing event, we can compare the right-hand end

of the blue graph with the green graph instead – seeing that the mirroring becomes even better.

(The break-adjusted series has a correlation of 0.83, which is very high.) In Appendix B we confirm

that the series does indeed exhibit a statistically significant structural break in the fourth quarter of

1992.

0

20

40

60

80

100

120

140

160

Average annual quarter-on-quarter growth rate for forthcoming 10 years: 1965Q1 = 100

10-yr index-linked gilt rate 1965Q1 = 100

10-yr index-linked gilt rate 1965Q1 = 100; Series break 1992Q4


- 16 -

Figure 3.2: Scatter plot of GDP growth versus gilt rate (raw values)


We focus on ten-year index-linked gilt yields and growth rates here. Five-year gilt yields can be

significantly affected by policy expectations — e.g. in a recession policy interest rates may be set low,

dragging down the five-year gilt yield. Since our data begins only in 1985, the use of twenty-year

values would make our dataset very short (just five years instead of fifteen). However, we

acknowledge that there is a compromise here. The actual growth rate could, in principle, deviate

materially from the underlying sustainable growth rate even over a ten-year horizon. For example,

one interpretation of our non-break-adjusted series could be that actual growth rates were below

sustainable growth rates during the 1980s but then above sustainable growth rates during the 1990s

(perhaps “catching up” on the “lost growth” of the 1980s). One implication of this reflection is that

it is not obvious, despite the higher correlation, that our break-adjusted series is really the better

series for correlating to ten-year-ahead growth rates.

3.1.3 Predictions of model

These caveats notwithstanding, the upshot of our analysis is that the close relationship that theory

predicts between the risk-free rate and the sustainable growth rate appears to be borne out in

practice. The sustainable growth rate of the economy appears to have been fairly stable from the

mid to late 1980s, risen somewhat in the early 1990s, and fallen fairly rapidly from the second

quarter of 1997 to below its late 1980s trough.

In the following graph, using the correlation between the break-adjusted series for the index-linked

gilt rate and the sustainable growth rate to model the sustainable growth rate, we assume the


- 17 -

sustainable growth rate was 2.5 per cent at the start of 1985 and that changes in the risk-free rate

and sustainable growth rates are proportionate to one another.8

8 Our model, explains yields by a constant, the change in regime occurring in 1992Q3, and GDP, as set out in the

following table. A more elaborate version of the model (which confirms the presence of a statistically significant

correlation between yields and GDP) and additional statistical tests are provided in Appendix B to this section.

Estimation Details

Dependent Variable YIELD

Method Least Squares

Sample 1985Q1 2001Q2

Included observations 66

HAC standard errors & covariance (Bartlett kernel, Newey-West fixed bandwidth = 4.0000)

Variable Coefficient Standard Error t-Statistic Prob.

GDP 0.725478 0.095041 7.633279 0.000000

BREAK -0.01073 0.000983 -10.91737 0.000000

C 0.020568 0.002454 8.380193 0.000000

Estimation Statistics

R-squared 0.840297 Mean dependent var 0.034323

Adjusted R-squared 0.835227 S.D. dependent var 0.006840

S.E. of regression 0.002776 Akaike info criterion -8.890969

Sum squared resid 0.000486 Schwarz criterion -8.791439

Log likelihood 296.402 Hannan-Quinn criter. -8.85164

F-statistic 165.7408 Durbin-Watson stat 0.733382

Prob(F-statistic) 0.000000

Technically, this is a model of levels. In the model represented in Figure 3.3, we assume that the sustainable growth

rate in 1985Q1 is equal to the actual 10-year growth rate for the next ten years ahead (2.50 per cent, versus a value

of 2.0 generated by the model in the table). Changes in the level of yields from our model then constitute changes in

the level of yields from this 2.50 per cent startpoint. The effect is that the levels in the model represented in Figure

3.3 are around 0.5 per cent above those generated from the model in the table. For this reason the modelled

sustainable growth rate in Figure 3.3 is described as “Normalised”.


- 18 -

Figure 3.3: Modelled sustainable growth rate versus gilts yield


So, according to our model, the sustainable growth rate peaked at about 4 per cent in the mid-

1990s, and had fallen to about 2 per cent by the end of 2000. The rate continues around 2 per cent

until 2002, when it starts falling again. There is a brief odd blip up in mid-2007, and then the spike in

late 2008 (which surely reflects a sudden rise in sovereign default risk – i.e. the model is breaking

down as the index-linked gilt yield is no longer nearly-risk-free). From the first quarter of 2009 we

also get a downward distortion, as quantitative easing is estimated by the Bank of England to take

perhaps 150 basis points off yields.

So we have some distortions from late 2008 that make it difficult to guess what happened next. In its

Bristol Water judgement, the Competition Commission proposes a range of 1-2 per cent for the

risk-free rate, implying, on our model, a sustainable growth rate of 1.35-2.07 per cent.

3.2 The Sustainable Growth Rate (and hence Risk-Free Rate) is Likely

to Increase during the Price Control Period

The government Office for Budget Responsibility estimates that the economy’s sustainable growth

rate is 0.5 per cent for 2012, and will rise to around 2.2 per cent (which would imply a risk-free rate

of 2.2 per cent) by 2016.9

We consider it plausible — indeed, likely — that the sustainable growth rate could return to these

levels during the next few years, perhaps even exceeding 2.2 per cent by the middle of the price

control period. That is not, of course, to say that we predict average economic growth might be

well above 2 per cent during the next few years or even over the 2010s as a whole — as we say, the

9 See, for example, Table 3.1 p42 Economic and Fiscal Outlook, December 2012, Office for Budget

Responsibility http://cdn.budgetresponsibility.independent.gov.uk/December-2012-Economic-and-fiscal-

outlook23423423.pdf. The OBR considers the long-term figure to be 2.3 per cent.

http://cdn.budgetresponsibility.independent.gov.uk/December-2012-Economic-and-fiscal-outlook23423423.pdf

http://cdn.budgetresponsibility.independent.gov.uk/December-2012-Economic-and-fiscal-outlook23423423.pdf


- 19 -

risk-free rate data imply an underlying growth rate averaging perhaps only around 1 per cent.10

Rather, we believe it plausible that, within the next few years, the average growth rate for the next

ten years or so after that point could be in the region of (or perhaps even higher than) the 2.2 per

cent the government proposes. We present this thought in a stylised way in the figure below. It

should be clear that if the actual growth-rate is low enough in 2013-17 or high enough in 2023-27,

then it is possible for the average growth rate of 2013-23 to be markedly below the average growth

rate of 2017-27.

Figure 3.4: Stylised representation of our contention concerning sustainable growth rates

3.2.1 Longer-term gilt rates imply a significant rise in ten-year yields by late 2016

The claim that the risk-free rate should be expected to increase over the next five years (to the mid-

point of the price control) is also supported by the term structure of bonds. The term structure of

yields on index-linked government bonds can be used to infer forward real yields. In particular, it is

possible to lock in the interest paid for borrowing and lending money in the future.

Given current interest rates it is possible to receive one pound at time t by investing the amount

, where t is the number of years from 0 to t and the annualised interest rate at time 0 for

borrowing over the period 0 to t. This can be financed by borrowing the amount over the

period from 0 to t+s.

The total amount of interest that is paid for borrowing one pound over the period t to t+s is

. This implies that at time 0 the forward interest rate for t to t+s equals

.

10 Of course, if there is an “output gap”, then in addition to the underlying trend rate of growth, the economy

might have the capacity to “catch up”, also, growing faster than its trend rate.


- 20 -

At 30th November 2012, the real implied ten-year-ahead yield on indexed linked UK gilts was –0.7

per cent.11 But the implied yield for the ten years from November 2017 was 0.37 per cent — a rise

of more than one percentage point in the ten-year yield over the next five years. In the most recent

regulatory determination available at the time of writing — that of Ofcom — the risk-free rate was

determined at 1.4 per cent (despite the very low rates on contemporaneous ten-year gilt yields) for

a charge control applying in the period up to 31 March 2014.12 An implied rise of one percentage

point in yields, during the period February 2017-2027 versus the period November 2012-2022,

could be expected to imply a roughly equivalent rise in the risk-free rate.13

Figure 3.5: Implied Ten-Year Forward Yields at November 2012

Source: Europe Economics calculations based on Bank of England data

Some of this expected rise in yields may already be implicit in Ofcom’s determination of a risk-free

rate notably above contemporaneous ten-year yields. And it is also true that gilt yields are known

to have a term structure that is only imperfectly understood. However, we make the following

observations:

Though a positive slope to the yield curve is normal, on government bonds the standard rise

from around a 10 to a 20 years horizon is of the order of 10-20 basis points. The curve typically

flattens considerably after around eight years. In blue in the following figure we see the yield

curve for UK gilts for July 2003. Between 10 and 30 years there is a 32 basis points difference.

11 Source: Bank of England 12 http://stakeholders.ofcom.org.uk/binaries/consultations/823069/statement/statement.pdf 13 We note that Bank of England interventions via Quantitative Easing had the aim of lowering long term gilt

yields, implying that these interventions had the effect of flattening the curve. This raises the possibility that

the steepness of the curve shown in Figure 3.5 understates the underlying steepness.


- 21 -

Figure 3.6: Yield curves for July 2003 and July 2012

Source: yieldcurve.com

By contrast, in red in the figure we present the same curve for July 2012. We note that the rise

in yields across the curve is considerably more and over a longer timescale than can usually be

attributed to pure policy choice effects (e.g. decisions to keep interest rates low in the short

term to smooth out economic fluctuations such as recession). An above-100 basis point rise in

the 6 to 16 year phase (and indeed extended beyond that, even still rising materially to 30 years)

indicates a significant and unusual effect. It is possible that some element of this is a rise in

liquidity premia, but it seems very likely that the overwhelming majority of this effect reflects an

expectation that ten-year yields will be much higher by 2017 than those yields are today.

The common belief that index-linked gilt yields will, in due course, rise applies to longer-term

yields as well as to ten-year yields.

Though the Competition Commission has raised concerns about twenty-year yields in past

judgements, its view was that these were likely to be distorted downwards, not upwards.14

3.3 Why the Sustainable Growth Rate is Likely to Increase

We observe that a figure of 2.2 per cent for the sustainable growth rate would, on our models,

imply a risk-free rate of about 2.2 per cent — slightly above the top of the range recommended by

the Competition Commission in its Bristol Water judgement. Thus, one way of expressing our

claim is that although, recently, the risk-free rate may have lain around the middle of the CC’s

Bristol Water range (as reflected in, for example, the 2011 Ofcom judgement), there is reason to

believe that over the next few years it could rise towards (or even above) the top of that range, as

the sustainable growth rate of the economy rises.

14 See, for example, paragraph 70 in

http://www.competition-commission.org.uk/rep_pub/reports/2010/fulltext/558_appendices.pdf


- 22 -

Is it credible that the long-term sustainable growth rate might rise in the way implied by longer-term

gilt yields, perhaps reaching the 2.2 per cent claimed by the government or even, thereafter, rising

higher, perhaps to the 2.5 per cent or so that has been the UK’s longer-term historic average? We

point to six key factors that suggest it might be:

Reduced public spending / taxation relative to GDP

A reduction in the level of government debt relative to GDP

A reduction in corporate sector debt relative to GDP

A reduction in household debt relative to GDP, and an end to the financial crisis

Extension to the retirement age

An increase in the rate of productivity growth in the public sector

We shall now consider each of these cases in turn. We emphasize that in each case what we

propose is that a relevant factor has arisen in recent years that would tend to depress the rate of

overall economic growth for long enough to cover an entire investment cycle — and thus, in the

economist’s sense of “long term” affect the long-term sustainable growth rate — but that can

reasonably be expected (as indicated in both longer-term gilt yields and official economic forecasts)

to be at-least-partially reversed by the middle of the price control period, at least in terms of its

effects upon the growth rate for the ten years ahead from that point.

3.3.1 Reduced public spending relative to GDP

There is extensive academic empirical literature on the relationship between levels of public

spending, tax and GDP growth. Broadly stated, the conclusion of this literature is that at above

about 25 per cent of GDP, increasing public spending further reduces the long-term growth rate of

the economy (especially if such increases take the form of greater government consumption

expenditure, as opposed to investment expenditure or transfers).

We emphasize that it does not, of course, follow that it would be politically desirable only to spend

25 per cent of GDP. After all, the extra spending produces potentially socially desirable outputs,

such as ameliorating poverty, ill-health or poor education, enhancing social inclusion, enabling the

government to project military force around the world allowing the nation to diffuse its values and

fight injustice, and many other such things. Perhaps at some level of GDP spend, the reduction in

growth is of greater social cost than the social benefits of the extra spending (so then there would

be three zones — one in which additional spending enhanced growth, one in which spending

reduced growth but was still desirable because the trade-off was favourable, and a zone in which

further spending increases reduced growth and did not produce social benefits of greater value than

the cost of such growth reduction). In this report we make no comment on these essentially

political questions.

Instead, for our purposes here we focus on the well-established and long-established empirical

results concerning public spending, taxation and growth rates.

Regarding the impact of public spending, two particularly important recent studies are the following:

Afonso, A. & Furceri D. (January 2008), "Government size, composition, volatility, and economic

growth", European Central Bank working paper 849: “a percentage point increase in the share of total

revenue (total expenditure) would decrease output by 0.12 and 0.13 percentage points respectively for

the OECD and for the EU countries”


- 23 -

Mo, P.H. (2007), "Government expenditure and economic growth: the supply and demand sides", Fiscal

Studies 28 (4), pp497-522: “a 1 percentage point increase in the share of government consumption in

GDP reduces the equilibrium GDP growth rate by 0.216 percentage points”

The literature on the impacts of taxation gives similar results. The definitive study in that literature

was that of Leibfritz, W., Thornton, J. & Bibbee A., “Taxation and Economic Performance” OECD

Economics Department Working Papers 176 (1997). They find that a 10 percentage point increase

in the tax to GDP ratio reduces the growth rate by 0.5 – 2 percentage points — or equivalently that

each additional percentage point reduces the growth rate by 0.05-0.2 percentage points. (The more

recent Afonso & Furceri paper quoted above finds that a one percentage point increase in the share

of tax in GDP reduces growth by 0.12 percentage points.)

The practitioner rule of thumb here is that each additional percentage point rise in sustained levels

of public spending/tax should be expected to take 0.1-0.15 per cent off the growth rate of the

economy.

Total managed expenditure in the UK reached a trough of 36.3 per cent of GDP in financial year

1999/2000.15 This was the lowest figure recorded since straightforwardly comparable records began

in the early 1960s. It peaked at 47.6 per cent in 2009/10 — a rise of 11.3 percentage points over a

decade.

Had such a level of expenditure been maintained, with taxes raised to match it, the rate of GDP

growth could be expected to be reduced as a consequence. However, the government plans to

reduce spending back to 40.5 per cent by 2015/16 and 39.0 per cent in 2016/7. If, for the ten years

following that point, spending were maintained at around 40 per cent of GDP, the sustainable

growth rate could be expected to be materially higher than during the high-public-spending period of

2008/9-2014/15, which is projected to involve an average level of around 45 per cent of GDP. If we

assume that taxes would have to be set on average no more than three per cent below spending

(e.g. according to the Maastricht sustainability criteria), a five percentage point reduction in long-

term spending relative to GDP would imply around a five percentage point reduction in taxes. At

the Leibfritz et al. figure of 0.05-0.2 percentage points off growth for each percentage extra taxes, a

five percentage point reduction in long-term tax rates implies a 0.25-1 per cent rise in sustainable

growth rates.

To see whether a sustained cut in average long-term spending on this scale is plausible, we note that

public spending was 40.9 per cent of GDP in 2007 and the ten-year average was below 42 per cent

of GDP for every ten-year period commencing each year between 1985/6 and 2001/2. It thus seems

entirely plausible that public spending will be materially lower, relative to GDP, from 2017-on than

has been the case in recent years.

3.3.2 A reduction in the level of government debt relative to GDP

In their August 2011 Bank for International Settlements paper, Cecchetti et al.16 analyse the impact

of various forms of debt upon growth rates. Their conclusions are that, beyond a threshold level,

debt is damaging to growth. That threshold level in respect of government debt is around 85 per

cent of GDP.

15 Source: Public Finances Databank, March 2012 version: http://www.hm-

treasury.gov.uk/d/public_finances_databank.xls 16 Cecchetti, S.G., Mohanty, M.S. & Zampolli, F. (2011), “The real effects of debt”, prepared for the “Achieving

Maximum Long-Run Growth” symposium sponsored by the Federal Reserve Bank of Kansas City, Jackson

Hole, Wyoming, 25–27 August 2011 —

http://www.kc.frb.org/publicat/sympos/2011/2011.Cecchetti.paper.pdf


- 24 -

On UK government definitions, UK general government gross debt relative to GDP is projected to

peak at 97.4 per cent of GDP in 2015/16, falling to 94.4 per cent of GDP by 2017/18.17 This

compares with 37.0 per cent in 2001/2. The average from 1990/1 to 1999/2000 was 44.1 per cent.

The previous peak on straightforwardly comparable statistics was 64.2 per cent in 1976/7. On

Cecchetti et al.’s definitions, public sector debt rose from 42 per cent of GDP in 1990 to 54 per

cent in 2000 and 89 per cent in 2010.

Cecchetti et al. find that an additional ten percentage points of GDP of debt, above the threshold,

reduces annual trend growth by around 0.1 percentage points. Although the UK’s debt level will be

above the threshold, the government’s plans to reduce debt relative to GDP could take debt closer

towards the threshold level, mitigating its damaging effect and thereby increasing growth.

3.3.3 A reduction in corporate sector debt relative to GDP

On Cecchetti et al.’s figures, UK corporate sector debt rose from 93 per cent of GDP in 2000 to

126 per cent in 2010. The threshold level for corporate sector debt, above which it reduces trend

growth, is about 90 per cent of GDP. Each additional ten percentage points of debt above this level

reduces trend growth by around 0.05 per cent. So being 30 per cent above the threshold would be

expected to reduce trend growth by around 0.15 per cent.

The UK corporate sector has already materially deleveraged during the recession. It is natural to

expect further deleveraging over the next five years, as, relative to 2005-7, corporate debt spreads

have risen dramatically increasing the relative attractiveness of equity versus debt.

If corporate sector debt were to return to its 2000 level by around the middle of the price control

period, that could therefore be expected to add a further 0.15 percentage points to trend growth.

3.3.4 A reduction in household debt relative to GDP, and an end to the financial

crisis

Cecchetti et al. believe that there should be a similar threshold level for household debt, similar to

that applying for government and corporate sector debt. They state that their best guess as to this

level is around 85 per cent of GDP. However, it should be noted that in their statistical tests,

though 84 per cent was their models’ highest likelihood value for the threshold, the results were far

from statistically significant.

A related possibility, which Cecchetti et al. did not (directly) explore, is that household debt has its

effect upon growth primarily through increasing the likelihood of financial crises. Banking sector

crises have a huge effect in their model: each additional year of crisis takes 0.27 percentage points off

annual growth for the succeeding five years.

UK household debt rose from 75 per cent of GDP in 2000 to 106 per cent in 2010. However,

household debt in the UK has been falling back since its 2007 peak.18 Further falls by 2015/16 could

take it below growth-damaging levels, reducing the risk of further financial crises and reducing the

growth-depressing debt overhang.

17 Source: Public Finances Databank, December 2012 version: http://www.hm-

treasury.gov.uk/d/public_finances_databank.xls 18 Source: Household Indebtedness in the EU, Report prepared by Europe Economics for the European

Parliament’s CRIS committee, April 2010.


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3.3.5 Extension to the retirement age

In the Cecchetti et al. model, a one standard deviation increase in the dependency ratio (the ratio of

the non-working to working population), or an increase of around 3.5 percentage points in that

ratio, is associated with a 0.6 percentage point reduction in future average annual growth.

Dependency ratios in the UK have been projected to rise significantly. The number of people of

state pensionable age was projected, by the government in 200919, to increase by 32 per cent from

11.8m in 2008 to 15.6m by 2033, whilst the number of working age is projected to rise by just 14

per cent from 38.1m to 43.3m.

Subsequently, the government has announced plans to accelerate rises in the state pension age —

reaching 66 in 2020 instead of between 2024 and 2026 as previously planned.20 It seems plausible

that announcements of further subsequent increases in pension ages will follow by 2015/16, reducing

peak dependency ratios from those currently projected.

3.3.6 An increase in the rate of productivity growth in the public sector

From 1998 to 2007 average annual public sector productivity growth was 0.3 per cent, whilst for the

private sector it was 2.3 per cent.21 It is perhaps natural that in a period in which public spending

rose rapidly, it was difficult to absorb large increases in spending whilst also increasing productivity.

With government consumption constituting around 22 per cent of GDP, if the value of outputs over

inputs grew 1 per cent more rapidly from 2015/16 onwards than in 1998-2007 — plausible given the

tighter spending growth, and the opportunity to catch up on private sector productivity growth as

the spending rises of 1998 to 2007 are finally adapted to — then that could add around 0.2 per cent

to GDP growth.

We observe because of the ways in which GDP is measured, increased productivity growth in the

public sector might not lead to rises in measured GDP growth on anything like this scale. However,

of course, gilt rates reflect the true underlying economic situation, not simply that measured.

3.3.7 Intermediate conclusion on the scope for a rise in the sustainable growth

rate

If all achieved together, the potential impacts we have described could be very large.

0.25-1 per cent for reductions in the long-term trend tax rate

A material impact from the reduction in government debt

0.15 per cent for the reduction in corporate indebtedness

An unclear amount for the reduction in household indebtedness

Some material amount for the reduction in the increase in dependency ratios

Perhaps 0.2 per cent for increased productivity growth in the public sector

All told, these values sum to more than 0.6-1.4 additional percentage points of average growth.

Perhaps it is ambitious to believe that the top end of this range could really be achieved in practice,

19 http://www.ons.gov.uk/ons/rel/npp/national-population-projections/2008-based-projections/statistical-

bulletin-october-2009.pdf 20 http://www.dwp.gov.uk/consultations/2010/spa-66-review.shtml 21 See Basset D., Cawston T., Haldenby A., and Parsons, L. (April 2010), Public sector productivity, Reform


- 26 -

and without any offsetting other factors reducing sustainable growth. Nonetheless, we contend that

the factors above do suggest that the government’s own projections could be credible by the middle

of the next price control period. That is to say, by the middle of the next price control period, it is

not totally unreasonable to believe that the sustainable growth rate for the UK economy could have

returned from the recent very low values implied by risk-free rates (perhaps as low as 1 per cent,

perhaps even lower) back towards the 2.2 per cent projected by the government itself or even

perhaps to the 2.5 per cent longer-term value for the UK.

A rise in the sustainable growth rate to 2.2-2.5 per cent would, for the reasons we have set out

above, be expected to be correlated with a rise in the risk-free rate from below 1.5 per cent (in line

with Ofcom’s most recent judgement) to some 2.2-2.5 per cent — broadly in line with risk-free rate

determinations of the early to mid-2000s, but above more recent ranges such as the 1-2 per cent of

the Bristol Water judgement.

Based on (a) an assumed sustainable growth rate of 2.2 per cent by the middle of the period (in line

with the predictions of the OBR for 2016); (b) the Competition Commission’s proposed 1-2 per

cent range in the Bristol Water case; and (c) the above-one percentage point rise in expected ten-

year yields, between the time of writing and 2017, implied by longer-term bond yields the average

risk-free rate during the price control period is therefore in the region of 2.0-2.5 per cent.

A risk-free rate of 2.0-2.5 per cent would be broadly in line with regulatory determinations just

prior to the financial crisis, but below that of the period 2000-2005. Thus, one way of thinking

about this range is that, if we anticipate that by 2017 at least the outlook ahead will be for a world

beyond the crisis, a figure of 2.0-2.5 falls in the range one should naturally expect, given the

precedent of pre-crisis regulatory determinations. Indeed, arguably it should not altogether be ruled

out that by 2017 even the 2.5-3.0 per cent range typical of determinations around 2000-2005 could

be possible — after all, it only took around six years for determinations to fall from the 2.5-3.0

range to below 2.0; it should not be inconceivable that in six years they should rise back again.

However, for now we suggest that, based on our assumed scenario that the acute phase of the crisis

will be over, a figure of 2.0 is the lowest currently plausible. We emphasize that we do not argue

that the current risk-free rate is 2 per cent (though that would be in line with some recent

determinations). Instead, our argument is that the risk-free rate should be expected to rise to 2 per

cent (or perhaps even above) by the period relevant to the price control.

3.4 The Equity Risk Premium

Evidence on the equity risk premium comes from a number of sources:

Historical analysis of risk premiums.

Estimates based on economic data.

Surveys of opinion (e.g. from academics, analysts etc.).

Regulatory precedent.

3.4.1 Historical values

Historical risk premia can be calculated using data on equity market returns and returns on

government debt. The definitive longer-term Dimson, Marsh and Staunton studies on the ERP have

given estimates for the arithmetic risk premium for the UK of 5.2-5.4.


- 27 -

Research by Dimson, Marsh and Staunton published in 2002 raised the bar for the both data and

methods used to estimate the ERP.22 The study carried out by Dimson et al. sought to address the

fact that many of the long-run empirical studies on the equity risk premium had been based on the

experience of the US only. Dimson et al. argued that, given how successful the US economy had

been, the US risk premium was unlikely to be representative. Thus, they extended the evidence on

the equity risk premium by examining data on bond and bill returns in 16 countries over a 102 year

period (1900-2002). Their results showed that the equity risk premium has typically been lower

than previous research had suggested.

The 2002 results are updated in the table below.

Table 3.1: ERP estimates and volatility levels 1900-2010

Geometric mean Arithmetic mean Standard error

Belgium 2.6 4.9 2.0

France 3.2 5.6 2.2

Germany 5.4 8.8 2.7

Ireland 2.9 4.9 1.9

Italy 3.7 7.2 2.8

Netherlands 3.5 5.8 2.1

Spain 2.3 4.3 2.0

UK 3.9 5.2 1.6

USA 4.4 6.4 1.9

Europe 3.9 5.2 1.6

World 3.8 5.0 1.5 Source: Dimson et al. (2011)

Long-term historical evidence therefore suggests an equity risk premium of 4-5 per cent for the UK,

depending on the weight given to arithmetic versus geometric averaging.

The standard view is that the cost of capital in the context of five year price cap regulation should be

based on arithmetic mean returns, rather than geometric mean returns. The key reason for this is

that the cost of capital is calculated at the margin, rather than inframarginally. Thus, what is

important is the expected rate of return of the marginal unit of capital. That is what the arithmetic

mean captures, the measure relevant to the opportunity cost of capital.

If there were mean reversion then there would be some case for the use of geometric means,

because doing so captures the underlying process behind the historical returns, as opposed to the

annual observations of return captured by arithmetic mean. However, in our view it is too much to

suggest that there is powerful evidence of mean reversion in stock markets. Indeed, our

understanding is that the considerable bulk of academic evidence suggests that developed economy

markets are weakly efficient over any significant timescale, and thus that, say, annual returns certainly

do not exhibit mean reversion.

We appreciate that it might not be correct simply to use arithmetic means of actual observed

returns, but, rather, arithmetic means of logarithms of the returns. This issue was considered in the

Joint Regulators study by Smither's & Co (2003). Smither’s & Co point out that it is very commonly

assumed that investment returns follow a lognormal distribution. The lognormal distribution

accounts for the truncated nature of the possible equity return distribution — the downside risk is

limited to 100 per cent of investment, whereas upside risk is not. In other words, the distribution of

returns is skewed, and assuming that is lognormal is a way to represent this skewness. As well as

being a common assumption, there is empirical evidence that realised returns follow a lognormal

22 Dimson, Elroy, Marsh, Paul and Staunton, Mike (2002) “Global evidence on the equity risk premium”

London: London Business School.


- 28 -

distribution. For example, Andersen et al (2001) find that distributions of realised daily variances of

returns are highly non-normal and skewed to the right, but the logarithms of realised returns are

approximately normal.23

Smither’s & Co (2003) show that the geometric mean of returns corresponds quite closely to the

arithmetic mean of logarithms of returns. They show using the DMS data set that geometric mean

over-estimates the arithmetic mean of log returns by 0.2 percentage points. Therefore, if the

arithmetic mean of log returns is the preferred measure, under certain circumstances the geometric

mean could approximate to it.

However, there is no clear cut answer or agreement on the issue. Again, the clear aim should be to

derive an estimate of the arithmetic mean return. The ambiguity relates to whether the arithmetic

mean of normal returns or lognormal returns should be preferred. As discussed by Smither’s & Co

(2003), unless volatility of returns is constant and returns are unpredictable, assuming that the

arithmetic mean return is stable over time must mean that geometric mean return is not, and vice

versa. The above conditions do not hold together on the DMS sample. However, given the

variability in historical averages, there is no clear cut empirical answer to whether the arithmetic or

geometric mean (as an approximation to the arithmetic mean of logarithms of returns) is the one

that should be assumed stable.

Normal regulatory practice has become to shade down from the DMS arithmetic mean, to reflect

the points raised above, without closely approaching the DMS geometric mean estimates. However,

we would emphasize that justifying any reduction from the arithmetic mean based on lognormality

raises the question of how skewness of returns (driving the lognormal characterisation of the

distribution) is taken into account elsewhere in the determination. Once we begin to model

skewness, we must take into account that investors could care about it. If investors do care about

the skewness of returns, it should be taken into account in more comprehensive way than using it

just as a basis of an argument when it seems convenient.

Thus, although here we adopt as our base estimate of ERP a value that shades down from the DMS

arithmetic mean, we emphasize that our use of such an approach is not conceptually separable from

our later discussions of the significance of skewness. If, for example, our contention that skewness

is material were not to be accepted, the case for a higher ERP than that we propose here would be

correspondingly and automatically strengthened.

3.4.2 Forwards-looking estimates

It is possible to estimate risk premia without resorting to historical data. For example, it is possible

to use formulas relating share prices to expected future dividends to produce forward-looking ERP

estimates. Alternatively, Duff & Phelps (2011) take the historical range of equity risk premia and

select a point within this range, depending upon the economic situation at the time.

Table 3.2 summarises recent forwards-looking estimates of the ERP.

23 Andersen, Bollerslev, Diebold and Ebens (2001): “The distribution of realised stock return volatility”,

Journal of Financial Economics, Vol. 61, No. 1, pp. 43-76


- 29 -

Table 3.2: Forward-looking estimates of ERP

Source Date Comments ERP %

Barclays Capital24 February 2012 Projection for UK 6.3%

Citigroup25 September 2011 Projection 5. 0%-7.0%

Duff & Phelps26 October 2011 US, conditional on economic situation 6.0%

J.P. Morgan27 October 2011 UK 7.0%

Bank of America Merrill Lynch28 October 2011 US 5.5%

Source: Europe Economics research

Forwards-looking estimates therefore suggest an equity risk premium in the region of 5.0-7.0 per

cent.

3.4.3 Surveys

Surveys directly question respondents (including academics and financial analysts) on their views on

the equity risk premium. Table 3.3 summarises recent surveys.

Table 3.3: Summary of surveys on the ERP

Source Date Comments Average ERP %

Fernández et al.29 2012 UK 5.5

Fernández et al.30 2009 Europe 5.3

Welch28 2009 Academic Equity Premium Survey 6.2

Fernández et al.28 2008 Europe 5.3

Welch28 2007 Academic Financial Economists 6.0

Source: Adapted from Fernández, Aguirreamalloa & Corres (2012)

Evidence from surveys therefore suggests an equity risk premium of 5.3-6.2.

3.4.4 Regulatory precedent

Regulators, including CAA, have analysed the equity risk premium in their regulatory cost of capital

determinations. Table 3.4 shows recent regulatory determinations of ERP.

Table 3.4: Recent UK Regulatory Determinations of ERP

Authority Year Sector/company ERP %

Ofgem 2012 Gas and electricity transmission, gas

distribution 5.25

Ofgem 2011 Transmission 4.75-5.5

Ofcom 2011 Openreach 5.0

24 Barclays, Equity Gilt Study 2012 (February 2012) 25 Citigroup 26 Duff & Phelps, Client Alert: Duff & Phelps Increases US Equity Risk Premium Estimate to 6% (October 2011) 27 J.P. Morgan, Country Risk Analysis (October 2011) 28 Bank of America Merrill Lynch, Considerations on Risk Free Rate & Equity Risk Premium in the Current Market

Environment (October 2011) 29 P. Fernández, J. Aguirreamalloa & L. Corres, Market Risk Premium used in 82 countries in 2012: a survey with

7,192 answers IESE Business School Working Paper (June 2012) 30 Quoted in Fernández et al (2012)


- 30 -

Authority Year Sector/company ERP %

Ofcom 2011 Mobile call termination 5.0

Competition Commission 2010 Bristol Water 4.0 to 5.0

CAA 2010 NATS 5.25

Ofwat 2009 Water 5.4

Ofcom 2009 Openreach (BT’s other activities) 5.0

Ofgem 2009 Electricity distribution 5.25

CEPA for Office of Rail

Regulation 2008 Network Rail

3.0 to 5.0 but may be as

high as 7

NIAUR 2008 SONI 4.5

CAA 2008 Heathrow and Gatwick (BAA) 4.5

Competition Commission 2007 Heathrow and Gatwick (BAA) 2.5 to 4.5

Sources: Respective regulator reports.

Recent regulatory precedent therefore suggests an ERP in the region of 4.75-5.5, up from around 4.5

before the financial crisis began with the most recent proposals by Ofgem at 5.25 per cent.

3.4.5 Effect of economic conditions on the equity risk premium if a poor

medium- to long-term economic outlook persist

There are reasons to expect the ERP to vary depending on economic conditions. When economic

conditions are weak, the equity risk premium (ERP) tends to be elevated. Evidence reported in De

Paoli and Zabczyk (2009) suggests that the size of the ERP depends on whether the economy is in a

period of stagnation or prosperity. In particular, investors seem to require higher premia during

economic slowdowns than during booms. This empirical regularity has been termed “premium

counter-cyclicality”.31

Subsequent results of Bekaert and Harvey (1995), He, Kan, Ng and Zhang (1996) and Li (2001)

confirmed these findings. Cochrane and Piazessi (2005) find that the term premium is counter-

cyclical in the United States while Lustig and Verdelhan (2007) document strong counter-cyclicality

in the exchange rate risk premium. The two most popular asset pricing models attribute this

variation either to counter-cyclical changes in risk aversion (Campbell and Cochrane (1999)) or to

changes in the volatility of the consumption process (Bansal and Yaron (2004)).

Thus, extensive empirical evidence supports the view that risk premia tend to be higher in recession

and stagnation periods. Cochrane and Piazzesi (2005) argue that the ERP increases by almost 20 per

cent in period of crisis.

For this reason, during the financial crisis, a number of regulators have accepted a temporary

elevation in the ERP. For example:

Ofwat, in 2009, adopted an ERP of 5.4, reflecting their consultants’ recommendation of a “crisis”

ERP of 6 and a non-crisis ERP of 5 with a 45 per cent weighting to the crisis value.

31 See B. De Paoli and P. Zabczyk (2009) “Why do risk premia vary over time? A theoretical investigation

under habit formation. Harvey (1989) showed that US equity risk premia are higher at business cycle

troughs than they are at peaks. Subsequent results of Bekaert and Harvey (1995), He, Kan, Ng and Zhang

(1996) and Li (2001) confirmed these findings. Cochrane and Piazessi (2005) find that the term premium is

countercyclical in the United States while Lustig and Verdelhan (2007) document strong countercyclicality

in the exchange rate risk premium. The two most popular asset pricing models attribute this variation

either to countercyclical changes in risk aversion (Campbell and Cochrane (1999)) or to changes in the

volatility of the consumption process (Bansal and Yaron (2004))


- 31 -

The CAA adopted an ERP of 5.25 for its 2010 NATS judgement, again reflecting its consultants

recommendation of a “crisis” ERP of 6 and a non-crisis ERP of 5.

Ofcom adopted an ERP of 5 in its 2009 Openreach judgement, reflecting a 50 basis points “uplift”

due to economic turmoil.

Ofgem is proposing 5.25 per cent in latest proposals for gas and electricity transmission and gas

distribution.

However, the elevation in the equity risk premium is not always as great as the fall in the risk-free

rate, so total market returns often fall in tough times. This reflects the point, discussed above, that

total enterprise returns are lower when economic times are worse, so unless returns to labour fall

disproportionately, total returns to capital fall, also. Thus, whilst Smithers & Co Joint Regulators

study of 200332 suggested that total market returns were 6.5-7.5 per cent, by the time of the Bristol

Water judgement the Competition Commission was using a figure of 5 to 7 per cent.

Should a poor medium- to long-term economic outlook persist, rather than the underlying longer-

term outlook improving by the middle of the price control period as we argue above, we would

assume it natural to extend the use of “crisis” levels for the ERP — probably in line with the 6 per

cent crisis figures deployed by Ofwat and the CAA.

It is not, however, so obvious that total market returns would, under these circumstances, fall

further. Although in the UK, labour’s share of total enterprise returns has been stable over the past

two decades, in many other European countries the trend has been downwards. If, by 2017 or so,

the UK were to have adopted a similar trend, as happened in the 1980s (which commenced with a

period of austerity widely seen as analogous to that expected for the early 2010s), then, by 2017 or

so, total market returns to capital might not have fallen even if total enterprise returns fall — in

principle they might even rise.

3.4.6 Effect of economic conditions on the equity risk premium if the medium- to

long-term economic outlook improves by the middle of the price control

period

We have argued above that, by the middle of the price control period, it is reasonable to believe

that official government projections for the sustainable growth-rate of the economy could be

delivered, and hence the risk-free rate rise. When economic conditions improve, although the

equity risk premium may fall back, it should not be expected to fall back as much as the risk-free rate

rises, so total market returns should be expected to rise.

Indeed, it is not even clear that the Equity Risk Premium should be expected to fall back to its pre-

crisis levels. After a major economic and financial crisis, one might expect lasting impacts on risk

appetites. It is also plausible that, even if trend growth might increase back to previous norms, the

outlook could involve greater economic volatility — i.e. although average long-term growth rates

might return to older norms, volatility in growth might not be nearly so low as it was during the so-

called “Great Moderation” of the mid-1990s to mid-2000s.

There are a number of reasons these things might be so:

32 Wright S., Mason R. & Miles D. (2003), A Study into Certain Aspects of the Cost of Capital for Regulated Utilities

in the U.K., Smithers & Co —

http://www.ofcom.org.uk/static/archive/oftel/publications/pricing/2003/capt0203.pdf


- 32 -

Policy-makers might be less willing or less able (e.g. because of greater concerns over maintaining

fiscal solvency or concerns about maintaining more buffers on monetary policy) to “smooth”

growth through macroeconomic stabilisation policies. We are not suggesting that all

macroeconomic offsetting of shocks — e.g. cutting interest rates or cutting taxes in response to

growth downturns — might cease; merely that the degree to which this is done might be less

than was the case from the mid-1990s to mid-2000s.

Investors might be more concerned about so-called “black swan” events, technically affecting

kurtosis in the distribution of returns.

The actual or perceived skewness of returns, or the ability to diversify skewness, might have

changed.

3.4.7 Conclusion on the equity risk premium

Based on the evidence above we therefore expect an equity risk premium of at least 5 per cent,

assuming that our approach to the significance of skewness is accepted, and noting that if that were

not the case then a higher ERP, around 6.0 per cent, would be justified. We note that our proposed

ERP is in line with or below the ERP used in most recent determinations and below the most recent

Ofgem proposal for electricity & gas transmission, and gas distribution (5.25 per cent).

3.5 Conclusion on Total Market Returns

Our analysis suggests that total market returns are at least 7.0 per cent. This consists of a risk-free

rate of at least 2.0 per cent and an equity risk premium of at least 5.0 per cent. We note that our

proposed Total Market Return is the same as the upper end of the Q5 determination. The Q5-

determined cost of equity implies an ERP of some 4.25-4.5 whilst the Q5 risk-free rate was the same

for the lower and upper bound estimates. Thus the Q5 Total Market Return was very close to the

same as our proposed Total Market Return here. The Competition Commission has argued that

when the outlook for the economy is stronger the Total Market Return should be higher. Given

that the outlook from 2017-on is likely to be materially stronger than was the outlook from 2008 on

that formed the backdrop to the Q5 determination, our Total Market Return is highly conservative.

The conservative nature of our position on total market returns is evidenced further by statements

of other authorities on cost of capital trends. For example, the Bank of England position is that

although the overall cost of capital (total market return) did fall dramatically during 2009, it is now

already above the levels that prevailed at the time of the Q5 determination, as can be seen in the

following Bank of England chart.33

33 We observe that, even with the early bounce-back, on these Bank of England figures the average total

market return for Q5 could still be below the Q5 determination, as we suggest above and below.


- 33 -

Figure 3.7: Bank of England calculations-based chart from MPC Member Ben Broadbent speech

of 12 September 201234

3.6 Skewness and Non-Diversifiable Skewness in Total Market Returns

In the standard CAPM equities’ excess returns are determined by the equity risk premium and

systematic risk, defined as the equity’s beta. In this sense, “risk” is equated with variance and

covariance. However, it is plausible that investors care about other aspects of returns’ distributions,

such as skewness.

If the distribution of returns is not symmetric, then it is said to be positively (negatively) skewed if

the right (left) tail of the distribution is longer than the left (right) tail (see Figure 3.8 below).

Figure 3.8: Skewness

34 http://www.bankofengland.co.uk/publications/Documents/speeches/2012/speech599.pdf


- 34 -

It is normally assumed in finance theory that, if investors have preferences skewness, then they

dislike negatively skewed assets as this implies more downside risk. Indeed, investors disliking

negative skewness is more compatible with Arrow-Pratt risk aversion than the standard CAPM

assumption that investors care only about returns’ mean and variance. A consequence of this is that,

if market returns are systematically and negatively skewed (i.e. if the negative skewness cannot be

diversified away) then (under standard finance theory, though not under the standard CAPM)

investors should require a skewness premium for holding assets that are negatively skewed. When

returns are skewed in ways that cannot be diversified away, they are said to be “co-skewed” or to

exhibit “co-skewness”.

3.7 Appendix: Technical Details Underpinning the Model

3.7.1 Break in the yields series

The gilt yield series discussed in section 3.1can be modelled as an ARMA (1,1) process

(autoregressive moving average) with a declining trend, named “T”. (We discuss stationarity issues

later.)

Table 3.5: ARMA (1,1) estimation results

Estimation Details


Sample (adjusted) 1985Q2 2001Q2

Included observations 65 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 0.044461 0.003579 12.42281 0.0000

T -0.000292 8.72E-05 -3.351631 0.0014

AR(1) 0.746950 0.092764 8.052165 0.0000

MA(1) 0.336549 0.139958 2.404639 0.0192









Inverted AR Roots .75

Inverted MA Roots -.34



- 35 -

Figure 3.9: Fitted and residuals of values for the ARMA (1,1) series process

The graph above indicates there 1992Q3 as a candidate date for a break in the series and indeed a

Chow test confirms this suspicion.

Table 3.6: Chow test on ARMA(1,1)

Test Details

Chow Breakpoint Test 1992Q3

Null Hypothesis No breaks at specified breakpoints

Equation Sample 1985Q2 2001Q2

Test Results

F-statistic 8.680055 Prob. F(4,57) 0.000000

Log likelihood ratio 30.91995 Prob. Chi-Square(4) 0.000000

Wald Statistic 39.88169 Prob. Chi-Square(4) 0.000000


We have repeated the Chow test after integrating the gilt series (i.e. taking its first difference, an

approach which also resolves potential non-stationarity issues) and modelling it as an ARIMA (1,1).

Table 3.7: ARIMA (1,1) estimation results

Estimation Details

Dependent Variable D(YIELD)




-.012

-.008

-.004

.000

.004

.008

.01

.02

.03

.04

.05

85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01

Residual Actual Fitted


- 36 -


C -0.000189 0.000357 -0.530306 0.597800

AR(1) -0.643664 0.186167 -3.457446 0.001000

MA(1) 0.844661 0.119575 7.063841 0.000000


R-squared 0.076295 Mean dependent var -0.000194







Inverted AR Roots -.64



Again, the Chow test confirms the presence of a structural break in the series in at 1992Q3.

Table 3.8: Chow test on ARIMA(1,1)

Test Details

Chow Breakpoint Test 1992Q3

Null Hypothesis No breaks at specified breakpoints

Equation Sample 1985Q3 2001Q2

Test Results

F-statistic 7.81411

Prob. F(3,58) 0.0002

Log likelihood ratio 21.72494

Prob. Chi-Square(3) 0.0001

Wald Statistic 53.73791

Prob. Chi-Square(3) 0.0000


3.7.2 Model relating yields to GDP growth

Our model explains yields by a constant, the change in regime occurring in 1992Q3, and GDP, as set

out in the following table.

Table 3.9: A simple model relating yields to GDP

Estimation Details



Sample 1985Q1 2001Q2

Included observations 66



- 37 -


GDP 0.725478 0.095041 7.633279 0.000000

BREAK -0.010731 0.000983 -10.91737 0.000000

C 0.020568 0.002454 8.380193 0.000000






Log likelihood 296.402 Hannan-Quinn

criter. -8.85164




Figure 3.10: Fitted and residuals of values for the simple model

In the model above, the inclusion of a dummy break variable is justified on the ground that the

results of a Chow test on the no-break version of the model indicates that there is a structural

break in the model in 1992Q3.

We have also estimated a more elaborate version of the model by including a first order lagged value

for yields and a moving average component. The results of this model are reported below and

confirm that the correlation between yields and GDP remains statistically significant at the 99 per

cent confidence level.

-.008

-.004

.000

.004

.008

.01

.02

.03

.04

.05

85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01



- 38 -

Table 3.10: A more elaborate model relating yields to GDP

Estimation Details





MA Backcast 1985Q1

Convergence achieved after 9 iterations



GDP 0.530202 0.103370 5.129185 0.000000

YIELD(-1) 0.196529 0.121390 1.618990 0.110700

BREAK -0.009016 0.001346 -6.696354 0.000000

C 0.018228 0.003724 4.894180 0.000000

MA(1) 0.780992 0.091691 8.517661 0.000000












- 39 -

Figure 3.11: Fitted and residuals of values for the more elaborate model

A caveat/concern regarding the results above is that if (as appears likely) the yields and GDP series

are non-stationary, the model might be capturing a spurious relationship as opposed to a long-run

equilibrium relationship. In fact Augmented Dickey Fuller (ADF) tests confirm that the yields and

GDP series are I(1) (i.e. they series are non-stationary at the levels, but their first differences are

stationary). Therefore, further analysis is required in order to test whether yields and GDP are

cointegrated, in which case we can conclude that the model describes a long run equilibrium

relationship.

We first notice that the Durbin-Watson (DW) statistics in Table 3.9 is materially different from

zero, which is a first indication that the series might be cointegrated (if the regression were

spurious we would expect a DW value close to zero). Furthermore, we have also performed an

ADF test on the residuals of the regression of Table 3.9 (for a cointegrating relationship we would

expect the residuals to be stationary). The resulting ADF test statistics of the residuals vary between

-3.68 and -3.75 (depending on the version of the test performed, i.e. with/without trend and/or

intercept). These values are larger than the asymptotic critical values (always at the 10 per cent

confidence level, sometimes at 5 per cent) for residual-based unit root tests for cointegration, hence

we can reject the hypothesis that the residuals are non-stationary.35

35 The values are -4.29, -3.74 and -3.45 respectively for 1%, 5% and 10% confidence, see Davidson, R. and J.G

McKinnon (1993), “Estimation and Inference in Econometrics”, Oxford University Press.

-.006

-.004

-.002

.000

.002

.004

.006

.01

.02

.03

.04

.05

85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01


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4 Debt Premium

4.1 Introduction

In Q5, the allowed debt premium was 105 basis points, of which 15 bps were issuance costs. To

examine what is the appropriate debt premium for Q6 we take evidence from a number of sources:

Spreads on Heathrow Airport Holdings’ own bonds.

Spreads on comparator airports’ bonds.

Spreads on UK utility companies’ bonds.

Our analysis suggests that Heathrow’s debt premium, including issuance costs, is 2.6 per cent.

4.2 Bond Spread Analysis

4.2.1 Heathrow’s bonds

Spreads on Heathrow Airport Holdings’ corporate bonds over Treasury gilts of the same maturity

(selected by Bloomberg) are shown in Figure 4.1. Spreads have generally decreased since their peak

in 2009 to a generally stable level between two and three per cent. However, some of Heathrow’s

more recent bond issuances have attracted consistently higher spreads.

Figure 4.1: Movements in Heathrow/BAA spreads after the last cut-off date (red dotted line)

Source: Bloomberg and Europe Economics calculations

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Summary statistics for Heathrow’s corporate bond spreads are provided in Table 4.1. This includes

spot spreads at 30/11/2012 and the mean, minimum and maximum spread of each bond over the

course of 2012. (Note that these bonds are ranked in order of maturity). In general Heathrow’s

bonds are ranked A- (on Bloomberg’s composite index of ratings agencies), though its issuances

since 2010 have been rated below this (at BBB and BB), and these bonds have attracted higher yields

and spreads. Using spot data from 30/11/2012 puts Heathrow’s average debt premium at 2.05 per

cent.

Table 4.1: Summary of Heathrow’s corporate bond spreads

Bond Maturity Rating Spread

30/11/2012

Mean

2012

Min

2012

Max

2012

BAA 5.85 11/27/13 27/11/2013 A- 1.24 1.88 1.15 2.52

BAA 5.85 11/27/13 27/11/2013 A- 1.22 1.88 1.15 2.52

BAA 3 06/08/15 08/06/2015 A- 1.18 1.72 1.14 2.57

BAA 12.45 03/31/16 31/03/2016 A- 1.74 2.27 1.68 2.62

BAA 7 1/8 03/01/17 01/03/2017 BB 4.15 5.65 3.96 7.25

BAA 6 1/4 09/10/18 10/09/2018 BBB 2.66 3.66 2.60 4.30

BAA 6 03/20/20 20/03/2020 BBB 2.67 3.62 2.63 4.37

BAA 9.2 03/29/21 29/03/2021 A- 1.80 2.27 1.76 2.69

BAA 5.225 02/15/23 15/02/2023 A- 1.75 2.27 1.68 2.76

BAA 5.225 02/15/23 15/02/2023 A- 1.75 2.27 1.68 2.77

BAA 7 1/8 02/14/24 14/02/2024 BBB 3.05 4.06 3.00 4.76

BAA 6 3/4 12/03/26 03/12/2026 A- 1.76 2.28 1.68 2.80

BAA 7.075 08/04/28 04/08/2028 A- 2.02 2.50 1.96 2.91

BAA 6.45 12/10/31 10/12/2031 A- 2.08 2.52 2.00 2.96

BAA 6.45 12/10/31 10/12/2031 A- 1.72 2.19 1.65 2.66

BAA 5 7/8 05/13/41 13/05/2041 A- 2.05 2.47 1.99 2.86

Average

2.05 2.72 1.98 3.33

Average BBB

2.79 3.78 2.74 4.48

Average A-

1.68 2.21 1.61 2.72


We assume that the required rating will be for bonds to be BBB+ or above. For 30/11/2012 spreads

on Heathrow’s A- rated bonds lie in the range 1.18 to 2.08 per cent, with an average spread of 1.68

per cent. For the same date, BBB rated bonds have spreads of 2.66 and 3.05 per cent, giving an

average spread of 2.79. The spread on a notional BBB+ bond would likely lie above the upper bound

of its A- rated bonds but below the lower bound of its BBB rated bonds, and thus in the range of 2.1

to 2.7 per cent.

4.2.2 Airports

Bond spreads were also available for a number of other major airport operators, namely:

Aéroports de Paris

Auckland International Airport

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Sydney Airport

Frankfurt Airport

Flughafen Zürich

Airport comparators’ corporate bond spreads are shown in Figure 4.2. There is a wide range in

comparators’ spreads that persists through time, with spreads ranging from around half to more

than four percentage points. The decline in spreads since 2009 seen in Heathrow’s bonds is

mirrored in some, but by no means all, comparators’ bonds.

Figure 4.2: Airport comparators’ corporate bond spreads 2009-2012


Table 4.2 provides summary statistics for the airport comparators’ bonds. As expected, lower rated

bonds (where a rating was available) had higher spreads. At 30/11/2012 spreads ranged from less

than one per cent to more than three per cent.

Table 4.2: Summary of airport comparator corporate bond spreads

Airport Bond Maturity Rating Spread

30/11/2012

Mean

2012

Min

2012

Max

2012

Paris ADPFP 4.100 03/11/13 11/03/2013 A+ 0.184 0.451 0.176 0.692

Paris ADPFP 6.375 01/24/14 24/01/2014 A+ 0.454 1.097 0.416 1.61

Paris ADPFP 2.375 06/11/19 11/06/2019 A+ 0.965 1.175 0.93 1.672

Paris ADPFP 3.886 05/10/20 10/05/2020 - 1.108 1.463 1.093 1.943

Paris ADPFP 4.000 07/08/21 08/07/2021 - 1.113 1.450 1.102 1.857

Paris ADPFP 3.875 02/15/22 15/02/2022 A+ 1.162 1.502 1.121 2.007

Paris ADPFP 3.125 06/11/24 11/06/2024 A+ 1.234 1.379 1.209 1.871

http://www.reuters.com/finance/stocks/overview?symbol=FHZN.S

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Airport Bond Maturity Rating Spread

30/11/2012

Mean

2012

Min

2012

Max

2012

Paris ADPFP 3.875 02/15/22 15/02/2022 A+ 1.162 1.502 1.121 2.007

Paris ADPFP 3.125 06/11/24 11/06/2024 A+ 1.234 1.379 1.209 1.871

Auckland AIANZ 7.190 11/07/12 07/11/2012 - 1.859 1.836 1.663 2.1

Auckland AIANZ 7.250 02/28/14 28/02/2014 - 1.155 2.081 1.155 2.932

Auckland AIANZ 7.000 11/27/14 27/11/2014 - 1.26 2.651 1.253 3.314

Auckland AIANZ 7.250 11/07/15 07/11/2015 - 1.455 2.410 1.451 3.695

Auckland AIANZ 8.000 08/10/16 10/08/2016 - 1.592 1.965 1.571 2.222

Auckland AIANZ 8.000 11/15/16 15/11/2016 - 1.335 2.795 1.335 3.432

Auckland AIANZ 5.470 10/17/17 17/10/2017 - 1.521 1.818 1.505 2.153

Fraport FRAGR 5.250 09/10/19 10/09/2019 - 1.809 2.145 1.755 2.438

Sydney SYDAU 8.000 07/06/15 06/07/2015 BBB 2.351 2.798 2.204 3.186

Sydney SYDAU 7.750 07/06/18 06/07/2018 BBB 2.864 3.3157 2.752 3.629

Zurich FHZNSW 4.500 02/18/14 18/02/2014 - 0.445 0.579 0.381 0.972

Zurich FHZNSW 2.250 05/05/17 05/05/2017 - 0.443 0.660 0.388 0.87

Zurich FHZNSW 1.250 07/03/20 03/07/2020 - 0.649 0.695 0.544 0.913

Average 1.24 1.69 1.2 2.15

Average BBB 2.61 3.06 2.48 3.41

Average A+ 0.91 1.21 0.88 1.68


At 30/11/2012 the spreads for Sydney’s BBB bonds were 2.35 and 2.86 per cent, while the spreads

for Aéroports de Paris’ A+ bonds were between 0.18 and 1.23 giving a potential range of

approximately 1.5-2.5 for ratings from BBB+ to A. The spread on a notional BBB+ rated bond would

likely be in the upper part of this range, in the region 2.0-2.5.

4.2.3 UK utilities

We also examine evidence on the debt premium of a number of UK utility companies. These are:

British Telecom

Centrica

National Grid

Northumbrian Water

Scottish & Southern Energy

Severn Trent

United Utilities.

Figure 4.3 shows the UK utility companies’ bonds since 2009. BT’s bonds yields are significantly

higher than the other comparators, in the region of two to three per cent since 2010, and have a

lower rating (BBB). As at 30/11/2012 all of the other comparators’ bond yields lay in the range of

0.8 to 1.79 per cent.

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Figure 4.3: Utility comparator corporate bond spreads 2009-2012


Table 4.3 shows the utility comparators’ bonds in greater detail.

Table 4.3: UK Utility companies’ corporate bond spreads (2012)

Company Bond Maturity Rating Spread

30/11/2012

Mean

2012

Min

2012

Max

2012

BT BRITEL 7.500 12/07/16 07/12/2016 BBB 1.463 1.46 2.02 1.41

BT BRITEL 6.625 06/23/17 23/06/2017 BBB 1.487 1.49 1.98 1.44

BT BRITEL 8.625 03/26/20 26/03/2020 BBB 1.732 1.73 2.10 1.65

BT BRITEL 5.750 12/07/28 07/12/2028 BBB 1.75 1.75 2.05 1.66

BT BRITEL 6.375 06/23/37 23/06/2037 BBB 1.764 1.76 2.08 1.68

Centrica CNALN 5.125 12/10/14 10/12/2014 A- 0.971 0.97 1.14 0.94

Centrica CNALN 5.500 10/24/16 24/10/2016 A- 1.191 1.19 1.48 1.16

Centrica CNALN 7.000 09/19/18 19/09/2018 A- 1.284 1.28 1.65 1.25

Centrica CNALN 6.375 03/10/22 10/03/2022 A- 1.288 1.29 1.61 1.26

Centrica CNALN 6.400 09/04/26 04/09/2026 A- 1.44 1.44 1.67 1.35

Centrica CNALN 4.375 03/13/29 13/03/2029 A- 1.371 1.37 1.51 1.26

Centrica CNALN 7.000 09/19/33 19/09/2033 A- 1.355 1.36 1.53 1.26

National Grid NGGLN 6.000 06/07/17 07/06/2017 A- 1.007 1.01 1.28 0.96





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Company Bond Maturity Rating Spread

30/11/2012

Mean

2012

Min

2012

Max

2012








National Grid NGGLN 5.500 07/24/13 24/07/2013 BBB+ 1.016 1.02 1.27 0.96

National Grid NGGLN 6.125 04/15/14 15/04/2014 BBB+ 1.062 1.06 1.31 1.04

Northumbrian Water NWGLN 6.000 10/11/17 11/10/2017 BBB+ 1.443 1.44 1.65 1.39




Scottish & Southern SSELN 4.625 02/20/37 20/02/2037 - 1.168 1.17 1.47 1.11

Scottish & Southern SSELN 5.750 02/05/14 05/02/2014 A- 0.952 0.95 1.18 0.89








Severn Trent SVTLN 5.250 12/08/14 08/12/2014 BBB+ 0.831 0.83 1.20 0.83




Severn Trent SVTLN 6.125 02/26/24 26/02/2024 - 1.786 1.79 1.86 1.69

United Utilities UU 6.125 12/29/15 29/12/2015 BBB+ 1.25 1.25 1.46 1.19





Average 1.33 1.55 1.27 1.87

Average A- 1.28 1.48 1.22 1.77

Average BBB+ 1.30 1.50 1.26 1.81

Average BBB 1.64 2.05 1.57 2.53


The spread on BBB+ rated bonds ranges from 0.83 to 1.46 per cent on 30/11/2012, with an average

of 1.30. The equivalent range for A- rated bonds is 0.95-1.44, while that for BBB rated bonds is 1.46-

1.76 per cent. This suggests a range for these bonds of approximately 1.0 to 1.8 per cent.

Debt Premium

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4.2.4 Conclusion on premium on existing debt

Our three sources of data give the following approximate ranges:

Heathrow’s own bonds indicate a debt premium in the region 2.1-2.7 per cent.

Other airports’ bond spreads are in the range of around 2.0-2.5 per cent.

Utility companies’ bond spreads are in the range of 1.0-1.8 per cent.

Noting that the debt premium ranges for Heathrow’s bonds and other airports lie in approximately

the same range, we suggest that Heathrow’s debt premium is in the range 2.1-2.7 per cent.

4.3 Issuance costs

In its November 2007 report on the Heathrow and Gatwick price controls the Competition

Commission gave the then BAA group an allowance of 15 basis points for “ongoing commitment,

agency and arrangement fees paid respectively to lenders, rating agencies and arrangers of finance“,

as part of a total cost of debt of 3.55 per cent.36 The CAA’s concurred with this cost of debt and

allowance in its March 2008 decision.37 Issuance costs in these judgements represent 4.4 per cent of

the cost of debt excluding issuance costs.

Taking a risk-free rate of 2-2.5 and debt premium of 2.5-3, that implies a total cost of debt of 4.5-5.5

before issuance costs. Applying a 4.4 per cent issuance cost would imply issuance costs of 20-24

bps. This was similar to the value determined by Ofwat in 2008/9.

However, issuance costs have been rising in recent years. Heathrow’s latest estimate of its issuance

costs are broken down into facility costs (costs related to mantaining a liquidity facility to deal with

unpredictability in capital requirements), the new issue premium (the difference between the implied

yield of Heathrow’s bonds as trading in the secondary markets, and the yield at which a new bond

would be issued), and issuance fees (those administrative fees charged by banks for managing the

issuance of debt), as follows:

Table 4.4: Breakdown of Heathrow’s actual issuance costs

New issue premium

Issuance costs

Bookrunner fees (e.g. paid to banks and credit rating agencies)

Listing fees, legal fees, prospectus costs, etc.

Overseas market costs

Facility costs

Facility costs: front-end

Facility costs: commitment fees

Total

Source: Heathrow

We understand that Heathrow has provided further detail to the CAA to support these estimates.

In the CAA’s approach, the cost of capital is based on a notional entity — perhaps an efficient new

entrant; perhaps a competitor — rather than on Heathrow’s actual costs. Hence Heathrow’s actual

36 Competition Commission. November 2007. “BAA Ltd - A report on the economic regulation of the

London airports companies (Heathrow Airport Ltd and Gatwick Airport Ltd)” 37 CAA. March 2008. “Economic Regulation of Heathrow and Gatwick Airports 2008-2013”.

http://www.caa.co.uk/default.aspx?catid=5&pagetype=90&pageid=8779

http://www.caa.co.uk/default.aspx?catid=5&pagetype=90&pageid=8779

Debt Premium

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issuance costs are of significance for what evidence they provide as to the likely costs of the notional

entity, rather than because the task is, in principle, to reflect Heathrow’s actual costs.

Heathrow and its owners deploy modern structured finance techniques in establishing and

maintaining their capital structure. It could be suggested that Heathrow’s actual issuance costs are

higher than those appropriate for a notional entity partly because Heathrow deploys such

techniques, which the notional entity is assumed not to. That is the approach implicit in our own

recommendation of 20 bps in our August 2012 report.

There is, however, a tension here, upon which the CAA should reflect. Heathrow does not deploy

structured finance techniques idly. It does so because the use of such techniques reduces its cost of

debt. The debt premium Europe Economics proposed in its August 2012 report was based upon

Heathrow’s actual debt premium (and indeed was at the bottom end of the range). Absent these

structured finance techniques — which do not come for free — Heathrow might expect to have a

higher debt premium. Accepting a reduced debt premium, but not accepting the issuance costs that

reduce it, is potentially inconsistent.

Taking account of the Ofwat determination and Heathrow’s latest estimate implies a range of

bps for issuance costs. We contend that 20 basis points is the lowest number a regulator could

plausibly propose, but would emphasize that in later phases of the price review we would firmly

expect to be arguing for a higher issuance cost than that, especially if structured financing were

assumed by the CAA.

We note that, so long as bonds’ credit ratings are similar, there may be significant variation in

issuers’ gearing that is not reflected in differences in the bonds’ debt premiums. Although high

gearing is one factor that may increase the risk of holding a bond, this and other factors that affect

total risk will determine both the bond’s spread and its credit rating. Two bonds with the same debt

premium and credit rating may therefore be issued by companies with different gearing to the extent

that other factors determining risk offset these differences in gearing. Evidence for this can be seen

in the following table, which shows that companies may have similar spreads for a particular bond

rating, but marked differences in gearing.

Table 4.5: Gearing and spreads for selected bonds

Rating Gearing

Spread for A-

rated bonds

(bps)

Spread for B-

rated bonds

(bps)

United Utilities A- 60% 160

National Grid A- 50% 160

Severn Trent BBB+ 70% 155

Thames Water A-/BBB+ 80% 165 290

Southern Water A-/BBB/B+ 85% 200 370

Yorkshire Water A-/BBB 80% 160 280

BAA A-/BBB 82% 240 435

Source: Data provided by Heathrow Airport Holdings Treasury Department

4.4 CEPA’s debt premium estimate

On p1, the CEPA report proposes a debt premium of 150-200 bps. This number is defended on

p11ff, section 4.1.2 of the CEPA report — though there appears to be a typographical error in the

conclusion on p17, which on the face of it proposes a point estimate for the debt premium of 150

bps. The CEPA report defends no specific figure for issuance costs, but its overall WACC proposals

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imply a debt issuance cost of zero. By contrast, the Europe Economics report proposes a debt

premium of 2.5 per cent supplemented by issuance costs of 20 bps, although we note the Heathrow

estimate the actual costs to be 50-60 bps.

4.4.1 Convergence of evidence

An initial point to emphasize here is a convergence of evidence in respect of Heathrow’s actual debt

premium. Table 4.2, p14 of the CEPA report finds the “Unweighted BAA bond average spread on

gilts” to be 261 bps and the “Weighted BAA bond average spread on gilts” to be 238 bps, which on

the face of it might be taken as implying a range of 2.4-2.6 per cent, very similar to the 2.5 per cent

proposed in the Europe Economics report. Table 4.2 of the CEPA report also illustrates that many

Heathrow/BAA debt issuances of recent years have carried much higher spreads than this, at the

point of issue.

Thus the difference between the Europe Economics and CEPA reports does not arise in respect of

any material dispute about the Heathrow debt premium. Rather, it arises in respect of the weight to

be attached to the Heathrow debt premium, relative to the debt premium on other corporate

bonds of the desired rating, in determining the regulatory debt premium of the notional competitor

or new entrant that is being simulated by a price control.

Specifically, whilst the Europe Economics methodology places most of the weight on the Heathrow

debt premium, it nonetheless places some weight on the debt premium of comparator airports,

which correspond approximately to the range of Heathrow’s debt premium (we observe here that

comparator airports feature in the estimation of CEPA’s equity beta). Europe Economics places

almost no weight upon the bond spreads of utilities.

By contrast, the CEPA report estimate is based heavily on the bond spreads of non-airport bonds

and regulatory determinations outside the airports sector (with the exception of the DAA 2010-14

CAR decision). In particular, it treats the index used by Ofgem in indexing the cost of debt, under

the RIIO framework, as the most “relevant reference point for the overall cost of debt allowance”.

4.4.2 Points of methodological disagreement

Europe Economics would highlight a number of key areas of disagreement with CEPA in how it

produces its debt premium estimate.

First, bonds of different ratings in different sectors need not carry the same premium. There are a

number of reasons for this, but the most pertinent in the current case is that a bond rating provides

an indication of risk of loss on a bond, but the debt premium will include an allowance for the

correlation of that loss with the market as a whole — the debt beta. There is strong theoretical

reason for disputing that the debt beta of all bonds will be the same — some defaults will be more

correlated with the economic cycle than others.

Absent any strong evidence on the debt premia of airports, it might be reasonable to use average

corporate sector debt premium for bonds of the relevant rating as one’s best-guess of the debt

premium. But as we have shown in section 4.2, the debt premia of utilities are lower than those of

airports — not just Heathrow, but other airports, also. CEPA does not attempt to argue that the

higher Heathrow/BAA bond yields arise from any specific inefficiency of Heathrow or other airports.

The best guess for an airport debt premium should therefore be above the average for utilities —

which it is not in the CEPA methodology.

Debt Premium

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Second, CEPA treats the Ofgem iBoxx data as a good reference for “the overall cost of debt

allowance”. But Ofgem uses the iBoxx data as part of an indexation methodology, insuring regulated

firms against adverse movements in the cost of debt, which the CAA has not previously done in

respect of Heathrow and which Heathrow has argued against in respect of Q6.

Third, in its derivation of a debt premium from the iBoxx data, CEPA estimates the debt premium

over its risk-free rate estimate, not over gilts. This presumably reflects the notion that gilt rates are

distorted downwards from the risk-free rate by policy measures such as quantitative easing (as the

Bank of England argues). But the distortion created by these policy measures is intended (inter alia)

to temporarily reduce the cost of debt. So at least some of the distortion would be expected to

apply to bonds in general, not simply to gilts, and to unwind in due course. Consequently, the

forwards-looking estimate of the debt premium is better based on gilts (as CEPA does in its earlier

analysis) than on the risk-free rate estimate.

Fourth, we do not believe that a 150 bps range satisfies a common sense-check. It is now widely

acknowledged that debt premia during the mid-2000s were seriously distorted downwards — risk

was under-priced, especially the risk of debt. The notion that the debt premium could have risen

from 105 basis points only to 150 basis points does not seem credible.

4.4.3 Summary of position

Thus, in respect of the debt premium, the CEPA report broadly agrees with the Europe Economics

report regarding the spread of Heathrow bonds over gilts — around 250 basis points. The key

points of dispute regard

how much weight to place upon the debt premium of airports, relative to utilities and other

bonds; and

whether the debt premium is best measured relative to gilts or to the current best-estimate

now-cast of the risk-free rate.

4.5 Conclusion on Heathrow’s debt premium

The evidence presented here suggests that Heathrow’s premium on existing debt is 2.1-2.7 per cent,

while its issuance costs at the very least 20 bps. Taking the mid-point of the 2.1-2.7 per cent range

and including 20 bps issuance costs gives a debt premium point estimate of 2.6 per cent.

Developments at Heathrow and in the Airport Sector Since 2007

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5 Developments at Heathrow and in

the Airport Sector Since 2007

5.1 Introduction

This sections sets out the key developments that have affected the airport sector since 2007. These

have been grouped in three separate areas:

General macroeconomic context

Changes in demand

Regulatory context

5.2 Macroeconomic context

At the time of the Q5 decision, the UK economy was entering the beginnings of a credit crisis. Prior

to 2007, the economy was in a boom period and leverage was increasing across the economy and

particularly in the utilities sector. Many of the key building blocks for the WACC were based on the

seminal Smithers & Co (2003) paper38, which in turn considered data from 2002 and earlier —

significantly influenced by developments from the mid-1990s onwards.

The crisis began to take hold in mid-2007 — prior to the CAA’s Q5 judgement – and became

progressively worse through 2008, leading to the deepest recession since the 1920s. The quasi-

nationalisation of Fannie Mae and Freddie Mac in September 2008 was soon followed by the collapse

of Lehman Brothers. Fearing a paralysis of the payment system, governments in Western countries

intervened in the form of the nationalisation and quasi-nationalisation of large parts of the banking

sector, the reduction of interest rates to near-zero, and the running of large government deficits.

Levels of economic activity nonetheless fell.

Inflation has returned as an issue in the macroeconomic debate. In the UK the policy measure of

inflation (CPI) has been consistently above 3 per cent since the start of the financial crisis, reaching 5

per cent in late 2011.

5.3 Changes in demand

The year-on-year contraction in passenger numbers at Heathrow, which started in 2008, continued

to 2010. There are a number of potential contributors to this:

Economic recession

Air Passenger Duty (APD) was increased in November 2010, taxing passenger £12 for short-haul

and £170 for long-haul flights.

38 Smithers & Co (2003), A Study into Certain Aspects of the Cost of Capital for Regulated Utilities in the U.K.,

http://www.ofgem.gov.uk/Networks/Policy/Documents1/2198-jointregscoc.pdf


- 51 -

The volcanic ash disruption of April 2010, which essentially brought air travel across the UK to a

standstill and caused aviation revenue losses.

The year 2010 also saw the first in a series of strikes by BA cabin crew strikes.

Longer-term factors, such as increased awareness of the chance of world and regional pandemics

(e.g. global diseases such as SARS or foot-and-mouth disease in Europe), and since the 11

September 2001 terrorist attack, incidents are typically the periods of uncertainty and heighted

security levels. In particular, it is possible that some of these negative events are now perceived

by potential passengers as occurring more frequently.

That shocks affecting Heathrow negatively have become more frequent and more substantial in

recent can be seen in the following figure.

Figure 5.1: Shocks to Heathrow passengers relative to trend and seasonality

Source: Europe Economics calculations on BAA data

This chart depicts movements in Heathrow passengers after controlling for the effects of seasonal

factors and a time trend. The red lines circumscribe a confidence interval of one standard deviation

from the mean. We use these bounds to define a negative (positive) event as any instance where the

number of passengers is more than one standard deviation below (above) the mean. We note that

there have been two very large negative shocks, with the September 11 2001 terrorist attacks in the

United States and the impact of volcanic ash in 2010.

We analyse these same data in more detail in the following table.

Table 5.1: Table to show shocks to Heathrow passengers

No. of events Average size of event

Positive Negative Positive Negative

All sample 30 29 0.283 -0.381

Pre-2007 28 14 0.285 -0.387

Post-2007 2 15 0.256 -0.375 Source: Europe Economics calculations on BAA data


- 52 -

Here we see that since 2007 negative events have become significantly more frequent than positive

events — before 2007 we observe that negative events accounted for one third of all extreme

(positive and negative) events, whilst after 2007 negative events account for almost the entirety (88

per cent) of extreme events. We also note that the average size of positive and negative events has

decreased (in absolute terms) since 2007, but less for negative events (a decrease of 3 per cent) than

for positive ones (a decrease of 10 per cent).

5.4 Regulatory context

Of significance to UK airports is the Competition Commission investigation into the supply of

airport services by Heathrow/BAA, which commenced in 2007. The Competition Commission

published its final report on BAA’s seven UK airports, requiring, amongst other things, the

divestiture of both Stansted and Gatwick Airports, and the divestiture of either Edinburgh or

Glasgow Airport. The sale of Gatwick was completed on 3 December 2009. In February 2011, the

Supreme Court refused BAA permission to appeal against the Competition Commission’s decision in

the BAA investigation. Since then, the Competition Commission has consulted on whether there

have been any material changes in circumstances and on 19th July 2011 confirmed the requirement

for BAA to sell Stansted Airport and either Edinburgh or Glasgow Airport.39 Accordingly, BAA sold

Edinburgh Airport and has agreed the sale of Stansted Airport.40

The economic regulation of airports where there is judged to be no effective competition has been

reformed as of 2012 through the Civil Aviation Act.41 The reforms aim to improve the quality of

service that passengers receive at designated airports and contribute positively to economic growth.

They replace the existing statutory framework for regulation at designated airports with a more

flexible licence based system. With this current backdrop - and following consultation - the CAA

decided to extend Q5 by one year.

At the pan-European level, the European Airport Charges Directive (2009/12/EC) has come into

force in the UK.42 This Directive establishes a common framework for the regulation and setting of

airport charges across the European Union.

5.5 The Impact of Capacity Constraints and Regulation on Skewness

A capacity-constrained airport subject to price cap regulation would be expected to have skewed

returns, in particular because its upside risk would be limited, creating an asymmetry. Upside risk

would be limited by the interaction of the capacity constraint and the price cap. A capacity-

constrained supplier would normally be able to react to “good” times by raising prices — for a

capacity-constrained supplier “increased demand” means increased willingness to pay.

But a price-capped capacity-constrained supplier cannot raise prices in response to increased

demand. So upside potential is absent.

39 http://www.competition-commission.org.uk/press_rel/2011/july/pdf/39_11_baa_final_mcc.pdf 40 http://www.baa.com/media-centre/press-releases/baa-announces-sale-of-edinburgh-airport,

http://www.baa.com/media-centre/press-releases/heathrow-airport-holdings-announces-sale-of-stansted-

airport 41 http://www.legislation.gov.uk/ukpga/2012/19/enacted 42 http://www.legislation.gov.uk/uksi/2011/2491/made

Equity Beta

- 53 -

6 Equity Beta

In this section we shall argue that Heathrow’s equity beta is currently higher than was determined in

Q5, and that it will remain higher to 2017, with current data suggesting the correct equity beta is

1.3. We argue this as follows:

We show that the equity betas of Heathrow’s most relevant comparators have increased, and

calculate their asset betas from market data. When relevered to reflect Heathrow’s gearing, that

produces an equity beta of 1.3.

Next, we argue from Heathrow-specific data. We do not have equity data to observe for

Heathrow. We do have data that allow us to estimate what is called a “fundamental beta”.

However, fundamental beta data does not fully reflect the impact of skewness, and in particular

our data does not fully reflect changing expectations of skewness. Skewness effects are likely to

be material for Heathrow in Q6. Hence our point estimate derived from Heathrow-specific data

involves both fundamental beta analysis and skewness analysis. This produces an equity beta of

1.36.

The fact that two such profoundly different methods both produce an equity beta so close to 1.3

reinforces the robustness of this estimate.

6.1 Comparator Data

Where comparable data exist, the equity betas of comparators have risen since early 2006 (the end

of the data window used in Q5 for Heathrow’s equity beta).

6.1.1 Selection of Comparators

Given the absence of listed equity for Heathrow, one approach to determining the systematic risk of

Heathrow’s equity is to examine that of suitable comparators’ whose equity is listed. The following

major European airports are listed (and have been cited as comparators in previous determinations):

Charles de Gaulle airport (Paris)

Frankfurt airport

Vienna airport

Zurich airport

Copenhagen airport

To determine which of these airports are the most suitable comparators for Heathrow, we compare

them across the following criteria:

Passenger volumes. Only Charles de Gaulle and Frankfurt have passenger numbers of similar

magnitude to those of Heathrow. The table below shows the comparators’ passenger numbers

for 2012:

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Table 6.1: Airport passenger numbers (2012)

Airport Passengers (million)

Heathrow 69.4

Charles de Gaulle 61.0

Frankfurt 56.4

Vienna 21.1

Zurich 24.3

Copenhagen 22.7

Source: Airports’ websites (August 2012)

Total assets. Only Aéroports de Paris and Fraport have assets comparable in scale to those of

Heathrow. The table below shows the comparators’ total assets. As the figures for Paris and

Frankfurt include minor airports (Orly and Frankfurt-Hahn), we have compared these to the

figures for BAA (SP) Ltd, which operates Heathrow’s other airports.

Table 6.2: Airport total assets (2011)

Airport Total Assets (£ million)

Heathrow & Stansted 12,530

Paris Charles de Gaulle & Orly 7,399

Frankfurt & Frankfurt-Hahn 7,708

Vienna 1,797

Zurich 2,620

Copenhagen 1,006

Source: Bloomberg & BAA (SP) Accounts

Hub status. Heathrow is the primary hub airport of British Airways and was, until recently, the

primary hub of bmi. All possible comparator airports serve as carrier as well. Charles de Gaulle,

is a hub for Air France, Fraport is a hub for Lufthansa, Vienna is a hub for Austrian Airlines,

Zurich is a hub for Swiss International Airlines and Copenhagen is a hub for Scandinavian Airlines.

Capacity constraint.s Like Heathrow, Vienna is constrained in its capacity and plan construction

of new runways and terminals. Fraport was subject to such constraints until October 2011 when

its new runway opened. According to JP Morgan Cazenove estimates, Paris-CDG’s capacity to

expand will be restricted in peak hours, but this can be alleviated by optimising use of its

runways.43 However, Heathrow’s capacity constraints are among the most severe, and JP Morgan

Cazenove notes that Heathrow’s constraints are likely to benefit Frankfurt (and, to a lesser

extent, Paris).

Passenger ratio (European : non-European). As an airport, Heathrow is distinctly international in

its nature, with over half of passenger journeys to and from Heathrow originating or terminating

outside Europe. Table 6.3 compares Heathrow and the other airports in this respect.

43 JP Morgan Cazenove, European Airports, Equity Research, 6th May 2011 p.59-60

Equity Beta

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Table 6.3: International nature of airports (2011)

Airport Ratio of passengers – Europe : Outside Europe44

Heathrow 47.9 : 52.1

Aeroports de Paris 61.1 : 38.9

Fraport 65.0 : 35.0

Vienna 88.3 : 11.7

Zurich 78.4 : 21.6

Source: Airports’ reports & websites, complied by Europe Economics

Operational environment. As can be seen, Heathrow has a higher proportion of non-European

passengers than any of the comparators. ADP and Fraport are moderately comparable, whilst

Vienna and Zurich are clearly incomparable, having only a small minority of non-Europe traffic.

Regulation. Like Heathrow, Aéroports de Paris is subject to explicit, multi-year regulation.

Fraport’s and Zurich’s regulation has been less formal. (In future, however, regulation across

European airports will be subject to greater harmonisation as the EU Directive on Airport

Charges is implemented.) 45

6.1.2 Summary

The following chart summarises the above discussion: As can be seen, Aéroports de Paris and

Fraport are, overall, markedly the most appropriate comparators for Heathrow.

Table 6.4: Summary of comparators’ similarity to Heathrow

Heathrow ADP Fraport Vienna Zurich

Copen-

hagen

Passenger

Numbers 69.4 88.1 96.6 21.1 24.3 22.7

Size

(Total Assets) 12,530 7,399 7,708 1,797 2,620 1,006

Hub British

Airways Air France Lufthansa

Austrian

Airlines

Swiss Intl.

Airlines

Scandin-

avian

Capacity

constraints Yes Somewhat

Until

10/2011 Yes No N/A

Passenger

Ratio 47.9 : 52.1 61.1 : 38.9 65.0 : 35.0 88.3 : 11.7 78.4 : 21.6 N/A

Similarity of

Regulation Yes Yes No N/A No N/A

6.1.3 Beta Analysis of Comparators

Figure 6.1, Figure 6.2 and Figure 6.3 shows our best-comparators’ equity betas calculated on

domestic, European and world market indices.

44 Figures for Heathrow, Fraport and Zurich include passenger origins and destinations; figures for Aeroports

de Paris and Vienna include passenger destinations. 45 Citigroup, European Airports and Airport Operators, 8th June 2011

Equity Beta

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Figure 6.1: Airport comparator equity betas calculated on domestic market indices (two and five

year rolling windows, 2005-2012)

Source: Europe Economics calculations using Bloomberg data

Figure 6.2: Airport comparator equity betas calculated on a European market index (two and

five year rolling windows, 2005-2012)


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Figure 6.3: Airport comparator equity betas calculated on a world market index (two and five

year rolling windows, 2005-2012)


We see large rises in betas since 2006 on equity betas measured against all indices.

In the table below we give equity betas alongside the companies’ gearing figures (defined as the ratio

of net debt to the sum of net debt and market capitalisation, adjusted to be between 0 per cent and

100 per cent) and the implied asset betas under the assumptions that debt beta is 0 or 0.1.

Table 6.5: Comparator airport betas, 30/11/2012

Two year Five year

Domestic

Index

European

Index

World

Index

Domestic

Index

European

Index

World

Index

Equity beta

Frankfurt Airport 0.779 0.926 0.996 0.841 0.878 0.857

Aéroports de Paris 0.698 0.829 0.846 0.896 0.962 0.958

Gearing

Frankfurt Airport 42.67% 42.67% 42.67% 36.11% 36.11% 36.11%

Aéroports de Paris 29.91% 29.91% 29.91% 29.22% 29.22% 29.22%

Asset beta (debt beta=0)



Asset beta (debt beta=0.1)



Re-levered beta (60%; debt beta=0)

Equity Beta

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Two year Five year

Domestic

Index

European

Index

World

Index

Domestic

Index

European

Index

World

Index



Re-levered beta (60%; debt beta=0.1)




Focusing upon the 0.1 debt beta case, the 2 and 5 year domestic equity betas for Fraport, re-levered

at 60 per cent gearing, are 1.1 and 1.3, whilst the 2 and 5 year equity betas for Paris re-levered at 60

per cent gearing, are 1.1 and 1.5. The overall range (1.1-1.5) has a midpoint of 1.3. The central

estimate is for the two most significant comparator airports (at 60 per cent gearing) is therefore 1.3

for equity beta (0.6 for asset beta, assuming 0.1 debt beta).

6.1.4 Evidence from Investor Reports

The table below shows evidence on equity betas for European airports taken from investor reports,

along with the values in this report.

Table 6.6: Asset betas for selected European airports

Publication date Fraport Asset beta ADP Asset beta

UBS (regional) 2007

1.118

UBS (global) 2007

1.127

Citi 2011 0.67 0.67

Citi 2012 0.68 0.77

Credit Suisse 2012 0.65 0.65

Source: Investor reports compiled by Europe Economics

By way of comparison, note that the central estimate of the asset betas for Fraport and ADP in this

report are 0.6. Conversely, with a debt beta of 0.1 and gearing of 60 per cent, an asset beta of 0.65

(the minimum seen in Table 6.6) would be equivalent to an equity beta of 1.475, whilst an asset beta

of 1.127 (the maximum seen in Table 6.6) would be equivalent to an equity beta of 2.668.

We can see that in other investor reports, the asset betas for our key comparators are typically

higher than those we have calculated above — indeed, our figure lies at the bottom end of the range

of these other estimates. This suggests that our relevered equity beta figure of 1.3 can be regarded

as conservative.

6.1.5 Conclusion: Value for Heathrow Equity Beta from Comparator Analysis

The estimate of Heathrow’s equity beta (at 60 per cent gearing) inferred from the two most

appropriate comparator airports is 1.3.

Equity Beta

- 59 -

6.2 Fundamental Beta Analysis

6.2.1 Fundamental betas and the effect of skewness

Whilst being potentially useful approach to gather evidence on the cost of equity of Heathrow based

on Heathrow’s own data, a key limitation of the fundamental beta approach is that is does not allow

capturing the potential effect of skewness of Heathrow’s cost of capital. The reason why is as

follows.

A fundamental beta model correlates beta with movements in accounting/financial variables for a

spread of firms within the market (in the case of our fundamental beta model, drawn from the

FTSE250 — as we shall explain below). Insofar as the market as a whole is skewed, such a model

will to some extent automatically embody the effects of skewness insofar as they are priced into

average returns and variance in returns in a systematic way. However, if a firm has a (non-

diversifiable) skewness that is disproportionate with the (non-diversifiable) variance of its returns, a

fundamental beta model will under-estimate the impact this higher skewness would have upon

required rates of return. We shall see that Heathrow does, indeed, have a skewness that is

disproportionate to that of its peers. It follows that one should expect a fundamental beta analysis

that did not take account of skewness effects to underestimate Heathrow’s beta.

We next explain in more detail the general principles underpinning why skewness may be

particularly relevant for Heathrow, and that Heathrow’s returns are indeed skewed. Once we have

explained these general points we go on in later sections to quantify their significance.

A capacity-constrained airport subject to price cap regulation would be expected to have skewed

returns, in particular because its upside risk would be limited, creating an asymmetry. Upside risk

would be limited by the interaction of the capacity constraint and the price cap. A capacity-

constrained supplier would normally be able to react to “good” times by raising prices — for a

capacity-constrained supplier “increased demand” means increased willingness to pay. But a price-

capped capacity-constrained supplier cannot raise prices in response to increased demand. So

upside potential is absent.

Associated with capacity constraints, Heathrow is becoming more dependent on one large customer

— British Airways — increasing the correlation of its own returns with those of BA. The increasing

dependence of Heathrow on BA can be seen in the following table.

Table 6.7: Market share of BA movements at Heathrow

2007 2008 2009 2010 2011 2012*

TOTAL AIRLINES

BA SHARE OF

MOVEMENTS

Source: Heathrow

(*) 2012 is Jan-Aug. Airlines flying less than 10 movements/year are excluded.

Since the airline sector is typically riskier than the airport sector, higher dependency on a single

airline will tend to increase Heathrow’s risk. Furthermore, with the BA/BMI merger we expect BA’s

percentage of movements to rise to in 2013.

Equity Beta

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Heathrow has conducted an analysis of Heathrow’s skewness by regressing passenger numbers and

estimated EBITDA (used as a proxy for returns because of the likely correlation between numbers

and revenues) on the UK market index and found that Heathrow is more negatively skewed than

other airports (see Figure 6.4). The analysis is based on the calculation of Harvey and Siddique’s co-

skewness which provides a measure of the skewness of an asset relative to the market.46 More

technical details on the concept of co-skewness and its implication for an assets’ cost of capital are

provided further below.

Figure 6.4: Heathrow’s Harvey and Siddique Co-skewness based on volume (passenger number)

data

Source: Heathrow

Based on the results of Figure 6.4 we observe that:

Historically, although Heathrow has had the most negative co-skewness compared to other

comparator airports, the absolute degree of co-skewness was relatively modest.

However, the co-skewness of Heathrow’s returns/volumes has become substantially more

negative since 2003.

Heathrow analysis suggests that the issue of co-skewness is of greater relevance to the Q6 price

control than might have been observable at the time of the Q5 control, and moreover the issue

seems to be of particular relevance for the airport sector.

46 The co-skewness measure used by Heathrow is the standardised co-skewness proposed by Harvey and

Siddique (2001), and is different from the gamma proposed by Kraus and Litzenberger (1976) that we have

adopted in our analysis and which is illustrated further below. Differently to the Kraus and Litzenberger

(1976) gamma, the interpretation of standardised skewness is independent of the underlying systematic

market skewness: a negative (positive) standardised co-skewness coefficient means that, if added to a fully

diversified market portfolio, an asset increase (decrease) the skewness of the portfolio, irrespectively of

whether this is positive or negative.

Equity Beta

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6.2.2 Use of fundamental betas in the Literature

There is a significant literature on the relationship between systematic risk and accounting and

financial variables. Lawrence et al. (2004) surveyed theoretical literature on the determinants of

equity betas. They note that expressions can be explicitly derived for the following variables:

leverage;

accounting beta;

earnings variability;

growth;

spread;

duration; and

capital adequacy (for banks).

They also note that a firm’s size can be intuitively expected to affect its beta, but do not provide an

explicit expression for this.47

In terms of empirical literature, Beaver et al. (1970) examined the correlation between the equity

betas of 307 firms for the years 1947-1965 and seven accounting risk measures:

dividend payout ratio;

asset growth;

leverage;

asset size;

liquidity (ratio of assets to liabilities);

earnings variability;

accounting beta.

Beaver et al. (1970) found that ranking portfolios of equities according to accounting risk measures

was equivalent to ranking portfolios according to their market beta. They also regressed company

and portfolio betas on payout ratios, asset growth and earnings variability, and found that the

predicted betas provided superior forecasts to the hypothesis that betas were constant.48

More recently, Hong and Sarkar (2007) provided evidence of the relationship between equity betas

and six accounting and market variables of 346 companies in the S&P 500 index for the period 1999-

2003. These variables were:

leverage;

correlation of earnings with the market;

earnings volatility;

ratio of market to book value of equity;

47 Lawrence, E.R., Mishra, S., Prakash, A.J. 2004. “A synthesis of theoretical relationship between systematic

risk and financial and accounting variables.” International Journal of Banking and Finance. Vol.2 No.1. 48 Beaver, W., Kettler, P., Scholes, M. 1970. ‘The association between market determined and accounting

determined risk measures.’ The Accounting Review. Vol.45 No.4.

Equity Beta

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earnings growth; and

company tax rate.

Of the coefficients that Hong and Sarkar estimate, all except leverage are statistically significant at

the five per cent level. 49

In a regulatory setting, Oxera (2006) used fundamental beta analysis to estimate individual asset

betas for Heathrow, Gatwick and Stansted Airports.50 Using a panel of data on 74 companies in the

UK utility, transport, retail and property sectors, Oxera analysed the relationship between asset

betas and five variables, namely:

the ratio of capital expenditures to free cash flows;

the ratio of capital expenditures to fixed assets;

earnings margin (before interest and tax);

market value; and

sector specific dummy variables.

6.2.3 Estimation Approach and Results

We now move to quantifying effects. In this section we calculate the uncorrected fundamental beta,

without taking account of skewness. In the next section we shall correct for skewness. Our analysis

uses data on FTSE250 firms for the period 2005 to 2010. The model is based on a number of

accounting variables as this ensures direct applicability of the results to Heathrow Airport. Where

possible, we used the full range of accounting variables found in the literature. The equation

estimated was:

βi,t = α0·SECi + α1·LEVi,t + α2·ACCi,t + α3·VOLi,t + α4·GROi,t + α5·CFCi,t + α6·CFAi,t +

α7·EMAi,t + α8·SIZi,t +εi,t

The variables used in this equation are described in Table 6.8. For the variable ACCi,t, the

correlation between firm and market earnings is used instead of firms’ accounting betas, which are

undesirably unstable due to the small number of available observations.51 Market earnings are

calculated as a market capitalisation-weighted average of firm earnings for FTSE250 companies. The

sector dummies are taken from Bloomberg’s industry sector field, in which Heathrow and all airport

comparators are classified as industrials.

Table 6.8: Description of variables

Variable Description

βi Firm’s equity beta from market data

SECi Sector dummy (Bloomberg sectors)

LEVi Average leverage

ACCi Correlation between firm’s earnings and market earnings

49 Hong, G., Sarkar, S. 2007. ‘Equity systematic risk (beta) and its determinants.’ Contemporary Accounting

Research. Vol.24 No.2. 50 Oxera. 2006. Stand-alone costs of capital of Heathrow, Gatwick and Stansted Airports p.14 51 Firm i’s accounting beta is defined as the estimated coefficient of the term βA in the equation ∆ Earningsi =

α + βA·∆ EarningsM, where EarningsM denotes earnings for the market index.

Equity Beta

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Variable Description

VOLi Standard deviation of changes in earnings

GROi Average change in earnings

CFCi Average ratio of capital expenditure to free cash flows

CFAi Average ratio of capital expenditure to fixed assets

EMAi Average earnings margin (ratio of earnings to revenue)

SIZi Average asset size

εi Random error

Our analysis is cross-sectional, using averages of data for 2005-2010 for the independent variables

and equity betas calculated on daily market data for the same period. We regressed the The results

of this estimation are shown below.

Figure 6.5: Fundamental Beta Estimation

Estimation Details

Dependent Variable βi

Method Panel Least Squares


Basic Materials 0.90 0.09 9.92 0.00

Communications 0.64 0.11 6.12 0.00

Consumer, Cyclical 0.87 0.06 13.48 0.00

Consumer, Non-

cyclical 0.57 0.06 9.90 0.00

Diversified 0.29 0.22 1.35 0.18

Energy 1.02 0.14 7.47 0.00

Financial 0.79 0.08 9.70 0.00

Industrial 0.82 0.06 13.34 0.00

Technology 0.75 0.08 9.03 0.00

Utilities 0.12 0.21 0.58 0.56

LEVi 0.45 0.14 3.32 0.00

ACCi 0.07 0.06 1.26 0.21

VOLi 4.69E-03 0.00 4.02 0.00

GROi -1.81E-03 0.00 -0.65 0.52

CFCi 6.43E-03 0.00 1.59 0.11

CFAi -0.03 0.03 -1.00 0.32

EMAi -9.79E-04 0.01 -0.17 0.87

SIZi 2.33E-05 0.00 1.90 0.06




Source: Europe Economics calculations based on Bloomberg data

Equity Beta

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We then multiply these values by the relevant values for Heathrow (then BAA) over the period

2005-2010 (annual averages). At the time of writing, consistent data on Heathrow/BAA’s gearing

was not available prior to 2008, so we have therefore used the 82 per cent gearing figure from the

previous section. We note again that cash-flow data is available on an annual basis, and is used in our

model.

Table 6.9: Application of fundamental beta analysis to Heathrow

Variable Coefficient Value Result

LEVi 0.45 0.82 0.37

ACCi 0.07 -0.41 -0.03

VOLi 4.69E-03 215.61 1.01

GROi -1.81E-03 34.67 -6.27E-02

CFCi 6.43E-03 -2.12 -1.37E-02

CFAi -2.56E-02 0.11 -2.77E-03

EMAi -9.79E-04 0.31 -3.02E-04

SIZi 2.33E-05 11,153.60 0.26

Industrial sector

dummy 0.82 1 0.82

Predicted beta

(sum of last column) 2.36


The model therefore gives a predicted equity beta of 2.36 at 82 per cent gearing. This can be re-

geared to the notional 60 per cent gearing level. Assuming a debt beta of 0.1 produces an estimated

equity beta of 1.1 at 60 per cent gearing. On the other hand, assuming a debt beta of 0 produces an

estimated equity beta of 1.06. Therefore, focusing upon a debt beta value of 0.1, the uncorrected

fundamental equity beta of Heathrow (at 60 per cent gearing) is 1.1.

6.3 Skewness Analysis

We now move to quantifying the effects of skewness on Heathrow’s cost of capital. In order to do

so, a number of steps are required. These are:

Assessment of whether skewness is systematic (i.e. non diversifiable).

Assessment of whether the potential impact of skewness on the cost of capital is a short-term

feature and immaterial in terms of the long-run cost of equity.

Quantification of the price investors attach to skewness (aggregate market skewness premium).

Assessment of whether airports’ returns tend to be more negatively skewed than market

returns, and whether the co-skewness of airports has increased in recent periods, making

skewness an issue of greater relevance to the Q6 price control than might have been observable

at the time of the Q5 control.

Quantification of the implication of skewness for the cost of equity of Heathrow.

6.3.1 Systematic skewness

Whether or not skewness can be diversified away depends on whether aggregate market returns

(i.e. the returns of a fully diversified portfolio) display systematic skewness. If aggregate market

Equity Beta

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returns are not skewed, the effect of holding a negatively (positively) skewed asset upon total

portfolio skewness can be diversified away by holding another asset (or portfolio of assets) with

opposite and offsetting skewness and this can be done without a cost in terms of mean return or

variance or mean-variance trade-off.

If, in contrast, aggregate market returns are systematically skewed, then the effect of holding skewed

assets cannot be fully diversified away by holding other assets without a cost in terms of mean

return or variance or mean-variance trade-off.

The UK equity market is treated as a fully diversified market in all standard regulatory cost of capital

analysis — indeed, the Market Risk Premium is even estimated from the Equity Risk Premium and

betas are calculated with reference to variance and covariance relative to the UK equity market.

The UK equity market as a whole — that fully diversified portfolio — exhibits skewness, as

illustrated in the Figure below.

Figure 6.6: FTSE All Share skewness and quarterly UK GDP growth (1990-2011)


6.3.2 Assessment of whether Skewness is a short-term feature and immaterial in

the long-run

UK equity market skewness may be positive or negative, depending on the market conditions at the

time. For instance, evidence from the FTSE All Share index suggests that the UK market is

negatively skewed in times of stable economic growth, whereas the market is positively skewed or

un-skewed during recessions and the early part of subsequent economic recoveries. Figure 6.6

shows the skewness of the FTSE All Share index (using a 12 month rolling window) alongside

quarterly UK GDP growth. During the period from 1993 to 2007, with GDP growth consistently

positive, the market is generally negatively skewed, 2003 being the only consistent period of positive

skewness. However, during the course of 1991- 1993 and 2008-2009 as the UK entered and then

exited recessions, returns appear positively skewed.

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

-2

-1

0

1

2

90 92 94 96 98 00 02 04 06 08 10

FTSE All Share skewness (12 month window)

UK GDP growth (quarter-on-quarter)

Zero

Equity Beta

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The correlation between economic cycles and the sign of market skewness implies that it is possible

to identify periods in which market skewness is zero as times of positive and negative skewness

cancel out. However, even if periods of zero skewness can be easily identified, it would be wrong to

consider this strong evidence of the absence of systematic skewness overall. This is the case for two

main reasons:

First, there is abundant empirical evidence that (at least over a sufficiently long period) aggregate

market skewness is negative.52,53

Second, the time frame of a price control review (i.e. five year) is likely to be short enough for

market returns to display systematic skewness. In fact, as Figure 6.7 below indicates, over the

period Jan-2000-March-2012, the five year rolling skewness of the FTSE ALL Share is consistently

negative and statistically significant (at the 10 per cent level) until the end of 2008. The skewness

ceases to be significant after 2008, i.e. in a period when the skewness is positive (see Figure 6.8).

Figure 6.7: FTSE All Skewness (5 years rolling windows: Jan-2000-March 2012) — statistically

significant values only


52 See e.g. Albuquerque, R. (2012) “Skewness in Stock Returns: Reconciling the Evidence on Firm Versus

Aggregate Returns”, Review of Financial Studies; Bris, A., Goetzmann, W. N., and Zhu, N., (2007), “Efficiency

and the Bear: Short Sales and Markets Around the World”, Journal of Finance 62, 1029-1079; Chen, J.,

Hong, H., and Stein, J. C., (2001), “Forecasting Crashes: Trading Volume, Past Returns and Conditional

Skewness in Stock Returns”, Journal of Financial Economics 61, 345-381; Kon, S., (1984), “Models of stock

returns — A comparison”, Journal of Finance 39, 147—65; Harvey, C.R. and Siddique, A. (2000)

“Conditional Skewness in Asset Pricing Tests”. Journal of Finance 55 1263—1296 53 We also conducted an analysis of the returns for the FTSE 100 Index since1984 (this index was used

because, for the FTSE 100 Bloomberg data is available for a longer period than for the All Share Index),

shows that, over a period of 27 years (which includes boom periods and recessions in the early 1990s and

late 2000s) market skewness is -0.31 and statistically significant at the 10 per cent level.

-.8

-.6

-.4

-.2

.0

.2

.4

00 01 02 03 04 05 06 07 08 09 10 11 12

FTSE All Share skewness (5 years rolling window, significant observations)

Equity Beta

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Figure 6.8: FTSE All Skewness (5 years rolling windows: Jan-2000-March-2012) — all values


The graphs above suggest that the returns of the UK equity market are, at least in normal times,

systematically and negatively skewed.

We noted that there is abundant empirical evidence that (at least over a sufficiently long period)

aggregate market skewness is negative and therefore not immaterial in the long-run. Moreover,

whilst noticing that it is possible to identify periods in which market skewness is zero (because

correlation between economic cycles and the sign of market skewness implies that times of positive

and negative skewness cancel out), the returns of the UK equity market are, at least in normal times

and when calculated over a 5-year time windows, systematically and negatively skewed.

6.3.3 Quantification of the price investors attach to skewness

In the standard CAPM model, agent utilities depend purely upon the mean and variance of returns.

The implication is that either investors have no preference over skewness (or indeed any higher

moment of the returns distribution, such as kurtosis), or returns are perfectly symmetrical (such

that there is no skewness and the distribution is fully characterised by its mean and variance).

If, instead, one is concerned with assessing whether investors attach a price to skewness and with

quantifying this price, the standard framework to use is the third moment CAPM. The third

moment CAPM is a natural extension of the CAPM model which assumes that investors (besides

having preference over returns’ mean and variance) also have preferences over the symmetry

(skewness) of returns.

The third moment CAPM was first introduced in a seminal paper by Kraus and Litzenberger (1976)

and is considered a widely accepted framework 54 The key idea behind the Third Moment CAPM is

54 Kraus, A. and Litzenberger, R.H. 1976. “Skewness Preference and the Valuation of Risk Assets”. The Journal

of Finance Vol.31 No.4.

-.8

-.6

-.4

-.2

.0

.2

.4

00 01 02 03 04 05 06 07 08 09 10 11 12

FTSE All Share skewness (5 years rolling window)

Equity Beta

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that, if market skewness is systematic (i.e. non-diversifiable), then the expected returns on a risk

asset i (in excess of the risk-free rate Rf) can be disaggregated into two different components:

A volatility-risk premium.

A skewness-risk premium.

Mathematically, this corresponds to the following equation:

E(Ri)-Rf = βi*V +γi*S [Eq. 6.1]

where V is the volatility-risk premium, S is the skewness risk premium, βi is the asset beta (which

denotes the co-variance of the asset returns with market returns), and γi is the asset gamma (which

denotes the co-skewness of the asset returns with market returns).55 The co-skewness of an asset

indicates the skewness of the asset’s returns in relation to that of the entire market. More

specifically, under the Third Moment CAPM specification of Kraus and Litzenberger, an asset with a

positive co-skewness has returns that are skewed in the same direction of the market skewness.

Therefore, if the market portfolio is negatively skewed, the inclusion of a positively co-skewed asset

contributes to the negative skewness of the market portfolio and the investor would require a

positive risk premium for that asset. If, in contrast, the market is positively skewed, the inclusion of

a positively co-skewed asset contributes to the positive skewness of the market portfolio and

therefore, the investor is willing to give up some returns for that that asset (see Table below). Table 6.10: Market Skewness and Asset co-skewness

Market (systematic)

skewness

Asset co-

skewness Implication

Positive Positive and

smaller than one

The asset contributes to the positive skewness of the market

but is less positively skewed relative to the market

Positive Positive and larger

than one

The asset contributes to the positive skewness of the market

and is more positively skewed relative to the market

Positive Negative The asset counterbalances the positive skewness of the

market

Negative Positive and smaller

than one

The asset contributes to the negative skewness of the market

but is less negatively skewed relative to the market

Negative Positive and larger

than one

The asset contributes to the negative skewness of the market

and is more negatively skewed relative to the market

Negative Negative The asset counterbalances the negative skewness of the

market

If the effect of holding a negatively (positively) skewed asset upon total portfolio skewness can be

diversified away by holding another asset (or portfolio of assets) with opposite and offsetting

skewness and this can be done without a cost in terms of mean return or variance or mean-variance

trade-off56 then the third moment CAPM would add nothing to the standard CAPM in a perfect

capital market (e.g. one in which there were a full span of returns distributions available). But if the

market itself exhibits overall systematic non-diversifiable skewness, the question arises what price, if

any, agents attach to co-skewness.

56 By mean-variance trade-off, we mean to refer to the trade-off between a zero-variance asset with a mean

return of the risk-free rate and an asset exhibiting whole-market basis variance but delivering the whole-

market return. One could envisage conditions under which it were possible to diversify away all skewness,

but this could only be done at the expense of a shift in the trade-off (e.g. transiting from a preferred low-

risk / low-return asset to an undesired high-risk / high return one). We shall not consider further whether

this case might have implications, or consider it further in any other way.

Equity Beta

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Provided that market skewness is systematic (and negative), for an average firm which is

representative of the underlying market risk, both beta and gamma must be equal to one. This

implies that, in a third moment CAPM framework, the equity risk-premium is expressed as the sum

of volatility risk premium and skewness risk premium57, i.e.:

ERP= V + S [Eq. 6.2]

Therefore, for an asset with co-skewness γi, the component of the cost of equity which is

attributable to skewness is γi*S.

In practice, both βi and γi can be estimated through the following empirical model:

Ri - Rf = a1 + a2*(Rm - Rf) + a3*(Rm –E(Rm))2 + ε [Eq. 6.3]

where Rm are the market returns, ε is the error term, and a1, a2, and a3 are the coefficients to be

estimated. The asset-specific risk parameters βi and γi can then be calculated directly from the

coefficients estimated in equation 1.3 through the following formulae:58

βi = a2+ a3 · (m3 / σ2)

γi = a2+ a3 · ((k4 – σ4) / m3)

where σ2, m3 and k4 are, respectively, the sample variance, skewness and kurtosis of the excess

market return.

6.3.4 The skewness premium

We have argued above that the UK equity market displays systematic negative skewness. This being

so, if at least some assets exhibiting negative co-skewness (i.e. roughly, with skew correlated with

market skewness) have systematically higher returns than assets with positive co-skewness (i.e.

roughly, with skew inversely correlated with that of the market), that would suggest that not only is

there skewness of returns that cannot fully be diversified away without consequences for mean or

variance, but agents also care about this and place a price upon it. That would suggest that the third

moment CAPM is a potentially relevant model.

In their original paper Kraus and Litzenberger (1976) estimated a co-skewness risk premium of

approximately 2.6 per cent. Since then various further studies have supported the case for a

positive co-skewness premium. For example, Conine and Tamarkin (1985) applied the Third

Moment CPAM to US utilities and found that the use of a Third Moment CAPM model (as opposed

to a standard CAPM) can add 1.3 per cent to the cost of equity of a typical utility. Harvey and

Siddique (2000) analysed 30 years of data and found a co-skewness risk-premium of approximately

1.9 per cent. Ang, Chen, and Xing (2006) find that the co-skewness premium can be as high as 6 per

cent.

We have carried out an original estimation of the skewness premiums based on daily returns data

covering a period of approximately ten years (i.e. 01/01/2001-31/03/2012). The analysis is based on

returns’ data of the companies composing the FTSE100 in January 2006 as this date (which lies

somewhere in the middle of the time window 01/01/2001-31/03/2012) increases the chance that the

57 Note that the skewness risk-premium V is positive because we are assuming that the systematic skewness

of the market is negative. The “premium” V would be negative if, instead, the market skewness was

positive. 58 We refer to Kraus and Litzenberger (1976) for the mathematical details underpinning these formulas.

Equity Beta

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companies considered would belong to the FTSE100 also at the beginning (i.e. Jan-2001) and at the

end (i.e. March-2012) of the period.

The first step of the analysis consists checking for the presence of systematic markets skewness.

The table below shows that the returns of the FTSE100 are not significantly skewed over the entire

time period (the t-statistics is lower than 1.63, i.e. the critical value at the 90 per cent confidence).

This is most likely due to the fact that periods of positive skewness (post 2009) and negative

skewness (pre 2009) trend to cancel out (see Figure 6.9). However the returns of the FTSEE100

display a statistically significant and negative skewness for the period 01/01/2001-09/09/2008. We

have therefore carried out the skewness premium analysis considering only data up to 09/09/2008

(i.e. to the period immediately after the quasi-nationalisations (conservatorships) of the Federal

National Mortgage Association and Federal Home Loan Mortgage Corporation on 6 September

2008 but immediately before the bankruptcy of Lehman Brothers on 15 September 2008).

Table 6.11: Skewness of the FTSE100

Period FTSE100 skewness No. of trading days t-stat

01/01/2001-31/03/2012 0.022 2844 0.49

01/01/2001-09/09/2008 -0.108 1944 1.94 Source: Bloomberg data and EE calculations

Figure 6.9: FTSE100 Skewness (5 years rolling windows: Jan-2001-March-2012)


For each company composing the FTSE100 we have used the Akaike selection criterion to test

whether the third moment CAPM performs better than a standard CAPM in explaining asset

returns. The analysis shows that for 24 out of the 102 companies considered the third Moment

-.8

-.6

-.4

-.2

.0

.2

.4

01 02 03 04 05 06 07 08 09 10 11 12

FTSE100 skewness (5 years rolling window)

Equity Beta

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CAPM is a preferable model.59 As the table below shows, the weighted average beta and the

weighted average gamma across the FTSE100 companies are, as expected, very close to one. 60

Table 6.12: Beta and Gamma estimates of the Assets Composing the FTSE100

Beta Gamma

Minimum 0.29 -2.60

Maximum 1.92 6.18

Median 0.9 1.16

Weighted Average 0.99 0.99 Source: Bloomberg data and EE calculations

Since the FTSE100 market returns are systematically and negatively skewed over the period

considered, and the third moment CAPM is preferred to a standard CAPM model for a material

number of assets, ceteris paribus, we would expect that assets with larger gamma estimates are

associated with higher returns because assets with large positive co-skewness values contribute

significantly to the systematic and negative skewness, as opposed to assets with small or negative

gammas. In order to test this assumption we have regressed the mean returns of each company on

the respective beta and gamma estimates. The output of the regression is reported below and

indicates that, as the theory predicts, both beta and gamma are significantly and positively correlated

with daily asset returns.

Table 6.13: The impact of Beta and Gamma on Company Returns61

Estimation Details

Dependent Variable Average Return

Cross-sections included 102


Beta 0.023506 0.004831 4.865190 0.0001

Gamma 0.014574 0.003016 4.831466 0.0000


R-squared -0.083439 Mean dependent var 0.045183

Adjusted R-squared -0.094273 S.D. dependent var 0.044708

Source: Bloomberg data and Europe Economics calculations

We also notice that coefficient of gamma is approximately 62 per cent of that of beta. This

relationship can be used as an apportionment rule to disaggregate the ERP into volatility premium

and skewness premium as follows:

59 The actual number of equities in the FTSE100 in January 2006 was 102, as two companies had more than

one equity. 60 Each company-specific weight has been calculated as the ratio of the company average market capitalisation

(over the 01/01/2001-31/03/2012 period) over the average FTSE100 market capitalisation. 61 The average daily return of a company composing the FTSE100 is reported in the table as “Mean

dependent variable”, and is equal to 0.045, which corresponds to an annual return of 11.9 per cent.

However the average daily return calculated of the FTSE100 over the same period is index is 0.014, i.e. 3.6

per cent in annual returns. This discrepancy should not be surprising because the mean return is calculated

as a simple arithmetic average of returns (as opposed to the weighted average on which the FTSE index is

based) and therefore high growth companies with a relatively small market capitalisation (and which have

therefore a small impact on the index) are given the same weight as larger companies with lower returns.

Equity Beta

- 72 -

ERP = V + S ERP = V + 0.62 * V

Since in an earlier section, we have argued for an ERP of the order of 5.0 per cent62, the

apportionment rule above implies a volatility premium, V, of 3.1 per cent (i.e. 5.0/(1+0.62)) and a

skewness premium, S, of 1.9 per cent (i.e. 0.62*3.1). We note that the skewness value we find is

identical to that found by Harvey and Siddique (2000).

6.3.5 Skewness in the Airport Sector

The next question for us is whether airports exhibit co-skewed returns. We have estimated the

Third Moment CAPM also for a set relevant airport comparators. The main purpose of this analysis

is:

To determine whether the returns of airports are significantly co-skewed;

To determine whether co-skewness has increased since the Q5 price control;

To this end, we have considered data for two time periods:

The period subsequent the cut-off date used in Q5 (i.e. 01/01/2006) and up to the date where

the market skewness displayed in Figure 6.7 ceases to be significant (i.e. 09/09/2008). Therefore

the entire period considered here is 01/01/2006-09/09/2008, and consists of 398 calendar days.

The period consisting of 398 calendar days preceding the cut-off period used in Q5, i.e. the

period 24/04/2003 - 01/01/2006 (this ensures that the pre-2006 and post-2006 samples are

balanced).

For each airport considered, the third Moment CAPM was estimated on: the domestic market

index, the European market index or the world MSCI index (the former was used for European

airports, whilst the second for non-European airports) and the FTSE All Share index.

As the table below shows, for both periods considered the skewness of the relevant domestic

markets is always negative (the only exception being the French market for the period pre-2006),

therefore, for all airports, the interpretation of gamma coefficients would be identical, i.e. a gamma

larger than one would imply that the airport’s returns are more negatively skewed relative to the

market.

Table 6.14: Market Skewness for Different Market Indexes

Period Market skewness

AT AU CH DE DK FR IT NZ UK EU World

24/04/2003- 01/01/2006 -0.49 -0.44 -0.26 -0.25 -0.20 0.04 -0.56 -0.36 -0.27 -0.25 -0.19

01/01/2006- 09/09/2008 -0.61 -0.28 -0.33 -0.47 -0.53 -0.30 -0.39 -0.01 -0.14 -0.25 -0.27

The beta and gamma estimates for the two periods are reported in the table below.

62 5.2 is the centrepoint of our 5-5.4 range

Equity Beta

- 73 -

Table 6.15: Third Moment CAPM of comparator airports

Pre-2006 (24/04/2003 - 31/12/2005) Post-2006 (01/01/2006-09/09/2008)

Beta Gamma Beta Gamma

Airports Domestic EU/

World UK Domestic

EU/

World UK Domestic

EU/

World UK Domestic

EU/

World UK

Auckland 1.05 0.20 0.20 1.14 1.47 1.10 1.18 0.16 0.03 * 0.94 0.76

BAA 0.65 0.49 0.65 0.57 0.57 0.57 n/a n/a n/a n/a n/a n/a

Florence 0.39 0.37 0.45 0.93 0.86 0.57 0.20 0.18 0.16 0.24 -0.53 -0.69

Fraport 0.48 0.60 0.67 0.81 0.93 1.25 0.96 1.01 0.91 1.44 1.54 2.10

Copenhave

n 0.69 0.43 0.49 -1.54 0.52 0.50 0.25 0.19 0.16 0.69 1.90 3.18

ADP n/a n/a n/a n/a n/a n/a 0.93 0.97 0.92 1.29 1.66 2.80

Sydney 0.89 0.22 0.30 2.07 2.07 1.96 0.83 0.64 0.39 1.15 0.11 0.61

Vienna 0.62 0.34 0.42 1.15 1.53 1.52 0.69 0.66 0.62 0.77 1.45 2.13

Zurich 0.36 0.33 0.44 0.81 0.59 1.32 0.59 0.55 0.51 1.44 2.18 3.25

Average 0.64 0.37 0.45 0.74 1.07 1.10 0.70 0.54 0.46 1.00 1.16 1.77

Notes; (1) All beta coefficients are statistically significant (at the 10 per cent confidence level).

(2) Significant gamma coefficients (at the 10 per cent confidence level) are in bold.

(3) Missing values are indicated by “n/a” and are due to the fact that there was no sufficient data for the given time period.

(*) The gamma estimate for Auckland airport obtained on the domestic index for the post-2006 period has not being reported because it is unreliably high.

Equity Beta

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The key results to be noticed are as follows:

There are more instances of significant co-skewness among airports in recent years (i.e. the post-

2006 period) compared to the period preceding the Q5 cut-off date.

Over the period subsequent January-2006 the co-skewness of airports appears to be, on average,

greater than that observed over the period preceding 2006. Moreover the values are, on average,

larger than one, meaning that airports’ returns trend to be more negatively skewed than market

returns.

Over the recent period, Fraport and Paris have the highest co-skewness (obtained on the domestic

indexes). Fraport (together with Zurich) has the highest co-skewness level (i.e.1.44), and Paris has

the second highest (1.29).

6.3.6 Quantification of the Implication of skewness for the cost of equity of

Heathrow

We have seen that airports tend to have negatively skewed returns. What about Heathrow? And is

its skewness more or less than that of a typical airport?

First, the analysis that Heathrow has conducted and that we reported in Figure 6.4 confirms the

results of our own analysis, namely that the issue of co-skewness is of greater relevance to the Q6

price control than might have been observable at the time of the Q5 control, and moreover the issue

seems to be of particular relevance for the airport sector. Second, it also suggests that:

Historically, although Heathrow has had the most negative co-skewness compared to other

comparator airports, the absolute degree of co-skewness was relatively modest.

However, the co-skewness of Heathrow’s returns/volumes has become substantially more negative

since 2003.

Noticing that:

In section 6.3.4 we have assessed the skewness premium (i.e. the premium of an asset skewed at

the market average, i.e. an asset with a gamma equal to one) as 1.9 per cent.

The gamma value for Fraport (obtained on the most recent data and on the domestic index) is

statistically significant and equal to 1.44. Moreover, Heathrow has a higher co-skewness than

Fraport: as Figure 6.4 shows, the difference (in absolute value) between Heathrow’s Harvey and

Siddique (HS) co-skewness and Fraport’s HS co-skewness varies between 0.28 (i.e. 0.28 =0.40-0.12)

and 0.62 (i.e. 0.62 =0.74-0.12). For reasons of conservativeness at this stage we proceed with a

difference of 0.28, which implies a gamma value for Heathrow of some 1.7 (1.44+0.28 = 1.72). This

would imply that, as a consequence of skewness, the required equity returns of Heathrow are 1.3

percentage points higher than those of an asset skewed at the market average (1.3 is calculated as

the skewness premium, 1.9 per cent, times the difference between Heathrow gamma and the

market gamma, i.e. 1.7-1.0=0.7).63,64

63 The calculation is based on equation 6.1. Recalling that the gamma of an asset skewed at the market average

is one, and that we have estimated the market skewness premium to be 1.9 per cent, it follows that the

component of market returns attributable to systematic market skewness is γmarket*S = 1* 0.19. Therefore, it

follows that the component of Heathrow’s returns attributable to the amount of skewness over and above

the market average is ΔγHeathrow * S = (γHeathrow- γmarket) * S = (1.68- 1.0) * 0.19 = 1.3. 64 We observe that our result is similar to that of Conine and Tamarkin (1985).

Equity Beta

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6.3.7 Conclusion: Value for Heathrow Equity Beta from Heathrow data

We used Heathrow-specific data to estimate an uncorrected fundamental beta (at 60 per cent gearing

level) of 1.1, which does not account for the effect of skewness. The skewness analysis of

comparator’s airports and Heathrow’s passenger volumes suggests that, as a consequence of

skewness, the required equity returns of Heathrow are 1.3 percentage points higher than those of an

asset skewed at the market average. An increment of 1.3 per cent with a 5 per cent equity risk

premium is equivalent to an increment of 0.26 to the equity beta. Working from the 1.1 fundamental

beta estimate, this implies a corrected equity beta (now taking proper account of skewness) of 1.36.

6.4 CEPA’s estimate of Equity Beta

This section compares CEPA’s estimate of equity beta with our analysis. We first notice that the

methodological approach used by CEPA for estimating the Heathrow’s equity beta shares a number of

similarities with the approach in the Europe Economics report. In particular:

Both analyses rely on the use of airport comparators’ data.

Beta estimates are based on daily data and do not make use of Bayesian or other adjustments.

Frankfurt Airport and Aéroports de Paris are considered the most relevant comparators, and

Heathrow’s equity beta estimates are obtained by re-levering the asset beta of these two airports.

Despite these methodological similarities there are material difference between our re-levered equity

betas and those obtained by CEPA. In the Europe Economics report we produce our estimate based

on 2 and 5 year betas, whilst CEPA considers 1, 3 and 5 years. In passing we question the robustness

of an estimate based on just one year of data in the context of the extreme market volatility of recent

years. For this reason we remain of the view that 2 and 5 year estimates should form the main basis of

estimation.

The estimates thus share one similar period — 5 years. In addition, we believe that CEPA assumes a

debt beta of 0, whilst in our central estimates we use a debt beta of 0.1 — we abstract from this point

in what follows. In what follows we focus upon differences in the 5 year estimate of asset beta, with a

debt beta of 0. We have reviewed a number of possible sources of this difference. These are:

More recent data

Differences in gearing calculation methodology

Difference in “market index” used

Difference in raw beta obtained from calculation (this last is the “unexplained” residual deviation)

A possible explanation for such discrepancies might be due to the fact that CEPA uses more recent

data. We have therefore updated our analysis to account for more recent data (up to 30 September

2012). Nevertheless our updated re-levered equity betas (based on 5 year of data) are 1.376 (based on

Frankfurt Airport) and 1.631 (based on Aéroports de Paris) and therefore remain significantly higher

than CEPA’s estimates, which are 1.165 (based on Frankfurt Airport) and 1.208 (based on Aéroports

de Paris) compared (see the table below).

Equity Beta

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Table 6.16: CEPA estimates vs Europe Economics Estimates

Asset beta (5 year)

Re-levered equity beta (5

year) at 60% gearing (debt

beta = 0)

Difference in re-

levered beta

(CEPA vs. EE)

CEPA

estimate EE estimate

CEPA

estimate EE estimate

Frankfurt Airport 0.466 0.550 1.165 1.376 -0.211

Aéroports de Paris 0.483 0.645 1.208 1.613 -0.405

In order to be able to reconcile such discrepancies we were provided with the values of CEPA’s raw

equity beta estimates and further details on the specific aspects of the estimation approach used by

CEPA.

We identified the following methodological differences as being potentially relevant in explaining such

differences:

Our beta estimates for Frankfurt Airport and Aéroports de Paris are based on domestic market

indices, whilst CEPA’s estimates are based on the World market index (FTSE All World).

The gearing definition we used to calculate asset betas is net debt over net debt plus market

capitalisation, whilst CEPA’s gearing definition is net debt over net debt plus total equity (i.e. the

gearing used by CEPA is a book value gearing).

We regard a market value approach to gearing as more standard than a book value approach, when

applied to de-levering raw values to asset betas. The market value of gearing embodies the same

overall market assessment of the value of the assets that drives the beta. The book value approach to

gearing is more standard when applied to re-levering from asset betas to equity betas, and might be

more defensible when estimating a beta from accounting data. We have nonetheless re-estimated betas based on different market indices and on different gearing

definitions. The results are reported in the table below.

Table 6.17: Beta estimates (5 year) based on alternative approaches

Frankfurt Airport Aéroports de Paris

EE’s benchmark estimates

Equity beta (based on domestic market index) 0.844 0.905

Gearing (based on market capitalisation) 34.8% 28.7%

Asset beta (assuming debt beta=0) 0.550 0.645

Re-levered equity beta (at 60% gearing level) 1.376 1.613

Estimates based on world market index

Equity beta (based on world market index) 0.859 0.962

Gearing (based on market capitalisation) 34.8% 28.7%



Estimates based world market index and book value

gearing

Equity beta (based on domestic market index) 0.859 0.962

Gearing (based on total equity) 41.7% 41.4%



Equity Beta

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Frankfurt Airport Aéroports de Paris

CEPA Estimates

Equity beta (based on world market index) 0.800 0.826

Gearing (based on total equity)* 41.8% 41.5%


Re-levered equity beta (at 60% gearing level) 1.165 1.208 (*) CEPA’s gearing figure have been imputed from equity betas and asset betas

Even after using the world market index to estimate raw betas and a book value definition of gearing

to calculate asset betas, discrepancies in re-levered equity beta remain material. This point is further

illustrated in the following table.

Table 6.18: Reconciliation Table

Raw difference in

calculation

variable

Difference for re-

levered (60%)

equity beta

Proportion of

difference

EE benchmark vs. CEPA

Frankfurt Airport -0.211

Aéroports de Paris -0.406

World index vs. domestic index

(gearing based on market cap.)

Frankfurt Airport 0.015 0.025 -12%

Aéroports de Paris 0.057 0.102 -25%

Gearing based on total equity vs.

gearing based on market cap. (based

on World index)

Frankfurt Airport 7% -0.148 70%

Aéroports de Paris 13% -0.306 75%

CEPA estimates vs. EE estimates

(based on World index and total

equity gearing)

Frankfurt Airport -0.059 -0.087 41%

Aéroports de Paris -0.136 -0.201 50% Source: Europe Economics calculations

From the table above we note that:

A significant portion of the discrepancies is attributable to difference in raw equity beta estimates

— when estimated on the FTSE All World index and on a common gearing basis, our raw beta

estimates remain significantly higher than CEPA’s estimates (for Frankfurt Airport our equity beta

estimate is 0.859 compared to CEPA’s 0.800, and for Aéroports de Paris we obtain 0.962 instead of

0.826.

The use by CEPA of a book-value gearing definition as opposed to the Europe Economics approach

based on market capitalisation explains a large part of the remaining difference between our

estimate and that of CEPA —book-value gearing figures are larger (with a difference of 7

percentage points for Frankfurt Airport and 13 percentage points for Aéroports de Paris) than

gearing figures based on market capitalisation.

The use by CEPA of the world market index, as opposed to a domestic market index, decreases (by

12 per cent for Fraport and 25 per cent for Aéroports de Paris) the discrepancies between our

benchmark estimates and CEPA’s figures — this is the case because raw beta estimates on the

Equity Beta

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world market index are greater than those obtained on domestic indices (thus our approach is

conservative).

We have cross-checked our equity beta estimates with those available from Bloomberg and this

confirms the validity of our estimates (the very slight differences between Bloomberg’s estimate and

our estimates are due to the fact that Bloomberg figures are calculated on data up to 25 October

2012, whilst our are based on data up to 30 September 2012).

Table 6.19 Bloomberg equity betas (estimates based on 5 years of daily data using the FTSE All

World index)

Bloomberg estimate EE estimate

Difference

(Bloomberg vs. EE)

Frankfurt Airport 0.858 0.859 -0.001

Aéroports de Paris 0.963 0.962 0.001 Source: Bloomberg and Europe Economics calculations

A last potential source of discrepancy might be due to the precise specification of the regression. We

have estimated betas based on log-returns (as opposed to raw returns), however the way in which

returns are expressed does not have a material impact on the estimates. Further details on the

estimation procedure we have adopted are provided in the appendix.

6.4.1 Conclusion

We have been unable to duplicate CEPA’s numbers and believe that they arise overwhelmingly from a

combination of a difference in gearing methodology and some as-yet-unexplained difference in the

calculation results — a difference that CEPA also has from the Bloomberg numbers — rather than

from some methodological difference. We hope this point of calculation difference can be resolved

soon.

6.5 Overall Conclusion on Equity Beta

First we have estimated an asset beta from direct market data of the most relevant comparators, and

re-levered it to reflect Heathrow’s gearing. This has produced an equity beta of 1.30. Next we have

calculated a fundamental beta using Heathrow-specific financial data and produced an uncorrected

equity beta of 1.1 which, once corrected for the effect of skewness produces an equity beta of 1.36.

The fact that two such profoundly different methods both produce an equity beta in the 1.3s

reinforces the robustness of this estimate, and leads us to conclude that the equity beta of Heathrow

(at 60 per cent gearing) is 1.30.

Overall WACC

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7 Overall WACC

7.1 Overall WACC Estimate

Our estimates of the components of Heathrow’s cost of capital, compared with those of the previous

determination, are shown in the table below, with the proposed increase disaggregated by its WACC

component.

Table 7.1: Differences in WACC – Q5 vs. proposed Q6

Q5

determination

WACC

estimate for

Q6 from this

report

Est. effect on

Q6 WACC

Est. % of total

Q6 WACC

rise

Low High

Risk free rate (%) 2.5 2.5 2 -0.58 -66

Equity risk premium (%) 2.5 4.5 5 +0.30 +34


issuance cost (%) 1.05 1.05 2.6 +0.93 +107

Equity beta 0.91 1.15 1.3 +0.58 +66

Cost of equity (post-tax) (%) 7.3 8.5

Tax rate* (%) 28 21 -0.36 -41

Cost of equity (pre-tax) (%) 10.2 10.8

Cost of debt (pre-tax) (%) 3.55 4.6

Gearing (%) 60 60 60

WACC (vanilla) (%)

WACC (pre-tax) (%) 6.2 7.1

We see first that our proposed Q6 value for the risk-free rate is below that determined in Q5. Risk-

free rate determinations have fallen in recent years. Indeed, some of the most recent (e.g. Ofcom’s

WBA determination of July 2011) have been below 2 per cent. We argue that the fall in the risk-free

rate is related to the fall in the UK’s underlying sustainable growth rate, and that since we (and the UK

government) believe that the sustainable growth rate will rise by 2017, the risk-free rate should

likewise rise. We emphasize that we do not argue that the current risk-free rate is 2 per cent (though

that would be in line with some recent determinations, such as Ofgem’s final proposals for electricity

and gas transmission and gas distribution from 2012). Instead, our argument is that the risk-free rate

should be expected to rise to 2 per cent (or perhaps even above) by the period relevant to the price

control.

Next, our ERP estimate, at 5 per cent, is above that used in Q5. We offer two observations on this.

First, our proposed ERP is in line with or below the ERP used in most recent determinations and

below the most recent Ofgem proposal for electricity and gas transmission, and gas distribution (5.25

per cent). Second, our combined Total Market Return (the sum of the risk-free rate and ERP) is 7 per

cent, the same as the upper end of the Q5 determination. The Q5-determined cost of equity implies

an ERP of some 4.25-4.5 whilst the Q5 risk-free rate was the same for the lower and upper bound

estimates. Thus the Q5 Total Market Return was very close to the same as our proposed Total

Market Return here. The Competition Commission has argued that when the outlook for the

Overall WACC

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economy is stronger the Total Market Return should be higher. Given that the outlook from 2017-on

is likely to be materially stronger than was the outlook from 2008 on that formed the backdrop to the

Q5 determination, our Total Market Return is highly conservative.

In combination, as can be seen in Table 1.1, our proposed Q6 risk-free rate and ERP tend to cut the

WACC from Q5 by 0.28 (0.30 – 0.58). Although the slightly higher ERP raises the WACC via the

cost of equity, the reduction in WACC associated with the lower risk-free rate, converted to a pre-

tax WACC basis, is greater.

The other generic (non-Heathrow-specific) parameter is the tax rate. We assume 21 per cent. That

assumption reduces the WACC, relative to the Q5 determination, by a further 0.36 per cent. In total,

the three generic parameters of WACC imply a drop in the Heathrow cost of capital of 0.64 per cent.

Falls in the determined cost of capital for other UK regulated entities have typically reflected falls in

generic parameters, not falls in company-specific parameters.

The rise proposed for Heathrow in Q6 is driven by the two Heathrow-specific parameters: the debt

premium and the equity beta. We observe that for other regulated entities, determined debt premia

have typically risen since 2007 whilst asset betas have not typically fallen. It is (as a matter of

definition) not possible for all companies to experience a fall in asset betas. But in particular, asset

betas for UK regulator sectors have not fallen across the board since 2007: some have stayed the

same, some may have fallen, some may have risen — each sector has its own specificities, as should be

expected from the intrinsically idiosyncratic nature of betas.

Consider Heathrow’s debt premium. We can see that the rise in the debt premium is much the most

significant driver of the increase in our proposed Q6 WACC versus the Q5 determination. That the

debt premium should be higher in Q6 than that determined in Q5 should be no surprise. Indeed, few,

if any serious, analysts would dispute that Heathrow’s debt premium would be higher than the Q5

determination even at present. The period leading up to the Q5 determination is now widely

recognised as one in which risks, especially risks associated with debt, were under-priced. It is

likewise widely recognised that even if debt premia were distorted upwards in late 2008 and 2009 by

extreme market conditions, the longer-term stable equilibrium position for debt premium, across the

corporate sector, will be markedly higher than in the run-up to Q5. Debt risk was significantly under-

priced. There has now been a correction.

It is possible that by 2017 debt premia will, though still above the Q5 determination levels, be lower

than at present. It is also possible that by 2017 they will be materially higher than at present. We are

not aware of a robust basis, in respect of the debt premium, for deviating from the latest figure as

providing the best forwards-looking estimate for this parameter. The debt premium, excluding

issuance costs, that we propose is 2.4 per cent versus 0.9 per cent in Q5. We propose at least 20

basis points of issuance costs versus the 15 provided for in the Q5 determination — essentially scaling

the Q5 value, percentagewise, relative to the cost of debt.

Finally, we consider the equity beta. We argue that the equity beta is already higher than that

determined in Q5 and that by 2017 it will remain higher. We emphasize that shocks affecting

Heathrow negatively have become more frequent and more substantial in recent years, that

comparators’ equity betas have risen since 2006, that Heathrow will be more capacity-constrained,

implying greater skewness in its returns, and that Heathrow is becoming more dependent on one large

customer in the form of British Airways. The sum of these impacts means that, even in Q5, the equity

beta may be higher than that in the Q5 determination. By 2017 capacity constraints are expected to

rise further, so raising the Q6 beta still more above the Q5 determination level.

Overall WACC

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7.2 CEPA’s estimate of the WACC

The following table summarises the key differences between the WACC components in the CEPA

report and those in the Europe Economics report.

Table 7.2: Differences in WACC – BA’s proposed Q6 vs. Heathrow’s proposed Q6

CEPA’s estimate

for Q6

Europe Economics’

current estimate for

Q6

Do EE’s estimates

fall within CEPA’s

range estimates?

Low High

Risk free rate (%) 1.5 2.0 2.0 Yes

Equity risk premium (%) 4.0 5.0 5.0 Yes


issuance cost (%) 1.5 2.0 2.7 No

Equity beta 1.0 1.2 1.35 No

Gearing (%) 60 60 60 Yes

Tax rate* (%) 23 23 23 Yes

WACC (pre-tax) (%) 4.66 6.56 7.4 No

(*) CEPA’s paper also provides a WACC figure based also on a tax corporate rate of 22 per cent however, for the sake of comparison, this

table reports only figures based on a tax rate of 23 per cent.

In respect of the risk-free rate and the equity risk premium, the Europe Economics report

recommendation lies within the CEPA report range. We observe that CEPA argues, on p11 of the

CEPA report, that a range of 1.5-2 per cent is, as we understand it intended to be “a reasonable

(arguably conservative) interpretation of the evidence as a whole” regarding the current risk-free rate.

By contrast, Europe Economics suggests that the current risk-free rate may be 1.5 per cent or lower

(though accepting that estimates as high as 2 per cent have featured in recent determinations), but

forecasts that the risk-free rate will rise to 2 per cent or higher by 2017. Thus, arguably, CEPA’s “now-

cast” of the risk-free rate is higher than Europe Economics’ current estimate, but Europe Economics’

“fore-cast” of the risk-free rate lies at the top of CEPA’s “now-cast” range. Although CEPA does not

explicitly discuss its forecast for how the risk-free rate might rise in the future, it appears to us that

our positions are not, at the stage, markedly different.

Regarding the equity risk premium (ERP), although in its main table on p1 the CEPA report suggests

that its ERP range is 4.0-5.0, it seems clear from the discussion on p18ff that CEPA puts a much higher

weight on the upper end of that range. In particular, in the debate about the weight to be given to

geometric versus arithmetic averaging in ERP estimates, CEPA comes down heavily on the arithmetic

side (CEPA report, p18), arguing that “the share for the arithmetic mean would be c. 94%, with just 6%

from the geometric mean. The appropriate unbiased estimate would therefore be 4.91%...As a result

we place greater weight in our analysis on the arithmetic mean.” Consequently, although CEPA makes

some reference to one Barclays study in justifying the lower end of its range, we feel it is clear that

CEPA’s analysis heavily favours the upper part of its range, and thus consider the CEPA and Europe

Economics positions broadly aligned on the ERP.

Both CEPA and Europe Economics assume a notional gearing of 60 per cent. CEPA considers

scenarios for the tax rate of 23 and 22 per cent, whilst Europe Economics considers only a 23 per cent

scenario, but there is no material difference in analysis on this point at this stage.

Thus the key differences between CEPA’s and Europe Economics’ WACC components relate to the

debt premium and the equity beta.

Overall WACC

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7.3 Aiming Up

The principle that there is an asymmetry of consequences between those of setting the cost of capital

too low and those of setting it too high is now well-established by regulators and the Competition

Commission. Too high a cost of capital, and consumers today pay a little more than would occur in a

competitive market. Too low a cost of capital, and consumers tomorrow miss out on the benefits of

investment and innovation that does not occur. The latter costs are recognised as significantly

exceeding the former. Consequently, it is now typically accepted that the regulatory cost of capital

should be set above the central estimate of the market cost of capital. The issue of precisely how

much to aim up is debated.

7.3.1 The CC’s approach

In its advice on the Q5 London airports price control, the Competition Commission aims up a number

of estimated parameters in the WACC calculation (such as the equity beta) basically by considering the

95 per cent confidence interval on the ground that if the true mean return is constant, then there is

approximately a 95 per cent chance that the true mean lies between two standard deviations

plus/minus the mean.65

7.3.2 The NATS determination

In the NATS determination, the consultants’ recommendation for the asset beta was explicitly

produced on the basis that, when market data are absent, the degree and nature of uncertainty

regarding asset beta estimation is more serious, and that this justifies “aiming up” to a greater degree.

That suggests that, in the current review, given that Heathrow does not have directly observable

market data available, the appropriate degree of aiming up might — reflecting the NATS precedent —

be expected, likewise, to be greater than would otherwise be the case.

7.3.3 Alternative approaches

The Q5 judgement was made in a period of considerable uncertainty in financial markets. Some

authors have argued that under more normal conditions, aiming up one standard deviation (equivalent

to aiming up at the 66 per cent confidence level) should suffice.

Whether the degree of aiming up in the current price control should reflect the precedent of the CC’s

previous recommendation or a more modest degree of aiming up might depend on the assumptions

used to estimate the central value. The higher the weighting given, in assessing that central value, to

data from current, depressed and volatile market conditions, the greater the uncertainties inherent,

and so the greater the likely appropriate degree of aiming up — perhaps closer to the 95 per cent

level recommended by the Competition Commission. We note that this would also be consistent

with the lack of directly observable market data and the NATS precedent.

Conversely, if the assumptions made are more forwards-looking, anticipating that by the middle of the

price control period market conditions should have stabilised, with generally higher returns, then

something closer to a one standard deviation (66 per cent confidence level) aiming up might be

sufficient.

65 See, for example, Competition Commision (2007), “BAA Ltd — A report on the economic regulation of the

London airports companies (Heathrow Airport Ltd and Gatwick Airport Ltd)”, Appendix paragraph 154,

Overall WACC

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7.4 Conclusion

To summarise, our contention is not that the Q5 determination under-states the current Heathrow

WACC. Our contention is that, by 2017, the Heathrow WACC will be higher than that at present,

with a point estimate of 7.1 per cent. The most important drivers of this rise are:

Total Market Returns (the sum of the risk-free rate and equity risk premium) are typically lower

when the economic outlook is worse, because expected returns to investment are then worse,

overall, so the opportunity cost of any particular investment is lower. After 2007 the investment

outlook deteriorated markedly as the UK’s sustainable growth rate fell. But by 2017 the outlook

from then forwards is expected to have improved. So by that point expected investment returns,

across the economy, will be higher, so (other things being equal) the opportunity cost of investing

in Heathrow will be higher. This will have its most material impact on the risk-free rate.

Cost of capital determinations since Q5 have typically involved falls in the cost of capital, relative to

pre-Q5 determinations. But we do not believe that Q5 over-stated Heathrow’s WACC. Instead,

Heathrow’s Q5-determined WACC may be approximately correct, because although risk-free

rates have fallen since Q5, debt premia and equity betas have risen, offsetting what would

otherwise have been a WACC drop.

The most significant driver of our increased WACC estimate for Q6, relative to Q5, is our

proposed increase in the debt premium. It is now widely recognised that debt premia in the period

leading up to Q5 were artificially and unsustainably low, and that increased debt premia in recent

years are not simply a symptom of passing market turbulence but, rather, reflect a genuine re-

assessment and re-pricing of debt risk. It is therefore to be expected that debt premia for Q6 will

be materially higher than those determined in Q5.

The equity beta for Heathrow should be assumed to have risen, because negative shocks at

Heathrow have become more frequent and more substantial in recent years, because equity betas

at the most relevant comparator airports have risen, and because Heathrow faces increasing issues

of capacity constraints, resulting in skewed returns that mandate an equity beta premium, and

capacity constraints (and hence skewness of returns) are likely to become even more significant

over Q6.

Appendix: Approach to Calculating Betas

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Appendix: Approach to Calculating

Betas

The general principles we followed for estimating betas are discussed below:

Data frequency ─ In principle daily data are preferred to weekly, monthly, or yearly data, because

they allow estimates on larger samples. In fact the use of daily data is endorsed also by Smithers &

Co (2003)66. We have therefore estimated equity betas on daily data, and we have carried out the

estimations controlling for both heteroskedasticity and serial correlation.

Estimation period ─ Equity betas vary over time. It is important, therefore, to choose an

estimation window that is as recent as possible, because today’s observation is the forward looking

estimate, However, the use of a wider window has the advantage of providing more robust

estimates. Consequently, we rely on estimates based on the last two years and the last five years

of data available. As a robustness check we have also calculated equity betas based on one year of

data, and we have provided one year two years, and five years rolling betas (graphs of rolling betas

are provided further below).

Industry returns ─ Smithers & Co (2003) suggests using the domestic market index (e.g. FTSE All

Share) for estimating the betas of UK utilities. Since our main comparators (i.e. Frankfurt Airport

and Aéroports de Paris) are listed on the German and French stock exchange, we have therefore

used the relevant domestic indexes (CDAX index and CAC All Share index, respectively) for our

benchmark estimates. We have then used the European market index (MSCI Europe Index) and

the world market index (FTSE All World) as a cross-check.

Our estimates are based on log-returns (as opposed to raw returns) — Smithers & Co favour

expressing returns in logarithmic terms, however we found that the way in which returns are

expressed does not have a material impact on the estimates.

When calculating asset betas we have used the average value of daily gearing figures (calculated over

the relevant period, i.e. 1 year, 2, years, or 5 years), where gearing at day t is defined as the ratio

between net debt (at day t) and the sum of net debt (at day t) and market capitalisation (at day t).

66 Wright, S., R. Manson, and D. Miles, (2003): “A Study into Certain Aspects of the Cost of Capital for

Regulated Utilities in the U.K.”