emerging markets debtsupplement.pionline.com/risk/_pdf/measuring_managing_em... · 2013-08-05 ·...

Perspectives September 2012

Arvind Rajan

Managing Director and International Chief Investment Officer Prudential Fixed Income

Martin Lawlor

Managing Director and Head of Risk Management Prudential Fixed Income

Emerging Markets Debt

Measuring and Managing Emerging Markets Fixed Income Risk Emerging markets (EM) fixed income assets, whether sovereign bonds, local currency bonds,

or corporate bonds, have enjoyed a decade of outstanding returns with only moderate risk and

occasional drawdowns. There are both structural and market-related justifications for this

strong performance, and the prospects for emerging assets in the future appear to remain

bright, especially relative to developed markets. However, any complacency regarding the

risks of investing in EM that might be fostered by a cursory glance at their recent performance

would be short-sighted and misleading. The risks are real, ranging from asset-class wide

market sell-offs, to individual country crises and currency devaluations, to corporate sector and

individual company defaults.

Further, the interactions between market and credit risk, and between systematic and country-

specific risk, are substantial, making disentangling and controlling each element a formidable

task. This paper will take on the practical challenge of describing a comprehensive framework

that captures the systematic, idiosyncratic, and basis risks associated with sovereign bonds,

local bonds and currencies, and their interactions. This exercise may be particularly apropos

today, because EM investments have historically been most vulnerable during periods of

global downturns, whether caused by liquidity crises or by garden-variety recessions. Both

risks are heightened given ongoing global deleveraging and the financial crisis in Europe.

We will begin by providing a recent historical perspective of EM risks seen through the lens of

asset-class-wide sell-offs and subsequent recoveries, as well as individual country crises. We

will then describe the emerging market sectors to be covered along with a taxonomy of risks,

and a typical risk budgeting process. We will also cover systematic risk, which is captured

using principal component analysis, and idiosyncratic and basis risk.

Emerging Markets Risk—A Historical Perspective

Before embarking on a classification of risks in an EM bond portfolio, it is useful to

look back at a few of the crises to which the asset class has succumbed—both those

that were country-specific and those that had an effect on the entire EM asset class.

A careful analysis would readily reveal that the history of lending to countries is an

oft-trampled path littered with the skeletons of unwary investors. In fact, the dangers

of investing in sovereign bonds, currencies, and local debt dates back at least 2,300

years, almost to the beginning of sovereign investing, and befalls not just EM but all

countries.1 Doing justice to these mishaps would take not just a thought paper, but

several volumes; luckily several competent authors have spared us this task.2 So,

instead, we will look at a more tractable recent period, without losing the larger

lessons of history.

1 Meticulous records kept by the Temple of Apollo in Delos, Greece indicate that 10 city states defaulted on loans from the

Temple in the fourth century BC. 2 Example: “This Time is Different – Eight Centuries of Financial Folly” by Carmen Reinhardt and Kenneth Rogoff, 2009.

Perspectives—September 2012

Emerging Market Debt—Sell Offs and Recoveries

Emerging markets fixed income assets may broadly be classified into sovereign, corporate (these first two

categories comprising hard currency assets) and local market bonds. All three of these emerging market sectors

have endured substantial sell-offs in the past. The tables below illustrate the major sell-offs that have occurred

historically in EM hard currency bonds and local market bonds. Since 1994, hard currency bonds have suffered

11 sell-offs of 5% or more, including three drops of over 27%—during the 1994 Mexican “peso” crisis, during the

Russian default in 1998, and after the 2008 Lehman default. In each case, the slump was not prolonged and the

index recovered within a few months on average, never taking longer than two years to catch up in total return.

This is even more remarkable considering that both Argentina (2001) and Russia (1998) made up substantial

parts of the index when they defaulted. Regardless of these comebacks, significant sell-offs across the entire

asset class must be regarded as “fat tail” risk events despite the diversity of countries in the EM indexes.1 Of the

three major sell-offs of >25% that occurred since 1994, one was associated with the Lehman default, while the

other two were coincident with country-specific crises in Mexico (1994), and Russia (1998), when the asset class

was not as well-diversified across countries. Therefore, along with systematic risk thresholds, country stress

limits, described later in this paper, can also help to mitigate the risks from such sell-offs.

EM Hard

Currency Debt

Sell-Offs and

Recoveries

(1994-2012)

Source: Prudential Fixed Income.

Local market bonds have had a similar history of quick recovery, as is illustrated in the table below, with extremely

short recovery times and double digit returns for nearly all of the subsequent 12-month periods.

EM Local

Currency Debt

Sell-Offs and

Recoveries

(2004-2011)

Source: Bloomberg. Returns stated are of the GBI-EM Global Diversified Index, as of 12.31.11.

1 Given a duration of about 6 years, a typical sell-off of say, 24%, would correspond to a widening of 400 basis points in spread over US Treasuries, or roughly three annual

standard deviations based on an index spread of about 400 basis points and a spread volatility of 35%.

Bounce Back from Trough

Defining Event YearPeak to Trough

Index Return Recovery TimeSubsequent

6-month Return

Subsequent

12-month Return

Eurozone Crisis 2011 -5.55% 2 months N/A N/A

Credit Crisis 2007–2008 -29.2% 9 months 33.8% 57.2%

Risk Reduction 2004 -9.1% 5 months 15.4% 24.2%

Fed Concerns 2003 -6.7% 4 months 11.0% 12.4%

Brazil Elections 2002 -6.3% 7 months 13.0% 31.0%

Argentine Crisis 2001 -5.9% 2 months 9.1% 10.8%

Profit Taking 2000 -6.6% 3 months 9.1% 19.0%

Russia Default 1998 -29.4% 13 months 26.3% 35.0%

Korea Crisis 1997 -10.6% 6 months 12.3% -5.0%

Profit Taking 1996 -8.4% 2 months 21.5% 41.0%

Mexico Crisis 1994 -26.5% 23 months 13.3% 4.0%

Sell-off PeriodPeak to

Trough (%)Recovery

Time

Subsequent 6-month

Return (%)

Subsequent 12-month Return (%)Start Date End Date

August 31, 2011 October 4, 2011 -11.4 6 months 11.6 n/a

November 4, 2010 November 29, 2010 -6.4 5 months 9.1 0.3

April 26, 2010 May 25, 2010 -7.7 3 months 15.2 21.3

January 6, 2009 March 9, 2009 -14.0 4 months 34.4 44.9

August 4, 2008 October 27, 2008 -27.7 11 months 19.6 43.8

July 23, 2007 August 16, 2007 -7.6 2 months 15.7 19.8

May 10, 2006 June 23, 2006 -10.5 6 months 18.2 28.1

March 8, 2005 March 28, 2005 -6.1 5 months 6.4 12.2

April 1, 2004 May 13, 2004 -7.1 3 1/2 months 16.5 27.3


Idiosyncratic risks refer to losses in a single country, industry or issuer, and are just as important as systematic or

asset-class-wide risks. Of these, the default, devaluation, or simply an EM country crisis are the most important

risks. These are well illustrated by the Mexican and Russian crises mentioned earlier, as well as by Argentina‟s

default in 2001. However, the taxonomy of country crises can vary, and the defining events in the first chart

shown on the previous page provide a sample set of the main types of crises. Among recent examples, Argentina

(2001) and Russia (1998) defaulted on both external and local bonds and also devalued their currencies, whereas

Mexico (1994) only devalued, and Ivory Coast (2010), only defaulted on external debt. Reinhart and Rogoff cite

sovereign, currency, and local bond crises of various types in their study, which explains that one does not need a

default to lose money in a country crisis. Even a crisis in a country‟s banking sector, or the bursting of an asset

bubble, can reverberate contagiously through the country‟s sovereign and local bonds, causing severe losses. A

good risk model would therefore need to adequately capture the effects of a country entering each of these states

of duress.

Risk Classification and Budgeting

The first task is to define the scope of assets whose risk the system must capture. A viable risk framework should

be designed to capture the risks of a typical emerging market bond portfolio, which may contain hard currency

sovereign bonds, local currency government bonds, and hard currency corporate bonds domiciled in an emerging

market. In addition, the risk system must be able to handle any derivatives typically found in such portfolios, such

as currency forwards, credit default swaps, U.S. dollar swaps, U.S. Treasury futures, and local interest rate

swaps.

Although there are many types of securities in such portfolios, the risks involved can be classified simply into two

main categories, which are illustrated on the following page. The first is systematic or market risk, which

describes broad exposure to three major risk factors: hard currency spreads, currencies, and interest rates (in

both external and local bonds). The second is idiosyncratic or tail risk caused by concentration in the portfolio,

including country risk, industry risk, and (single corporate) issuer risk.

Emerging

Markets

Debt Sample

Risk Budget

Sample1


In order to control the overall risk of the portfolio, we must define a common metric to measure and combine

these risks, and establish limits on the overall risk as well as on key sub-dimensions. This process, known as risk

budgeting, forms the bedrock of good risk management. For example, a typical budget for an active emerging

markets portfolio mandate might allow 275 basis points in active spread exposure, 175 basis points in active

currency exposure, and 125 basis points in yield curve (interest rate) exposure. The interest rate exposure

1 Total tracking error is less than the sum of the systematic and non-systematic tracking error because these two major sources of tracking error tend to diversify with each

other, thus lowering total tracking error: Square root of 320² + 140² = 350². 2 Under most market conditions, returns associated with these market risk factors tend to undergo small and independent day-to-day fluctuations, implying that mean and

variance measures explain most of the distribution of returns therefore we manage these risks via tracking error measures. 3 These risk factors generally carry substantial skew or tail risk. Because returns from these items are not adequately described by mean and variance, we supplement

tracking error measures with industry stress tests and issuer risk thresholds to monitor and limit the tail risk. 4 Issuer exposure is based on market implied ratings.

Rate Risk 125 bps

Currency Risk 175 bps

Spread Risk 275 bps

Country Stress Shock 200 bps

Illiquid Country Threshold (%MV) 2.5%

Industry Stress Shock 140 bps

Corporate Issuer Exposure:4

BBB (%MV) 2.5%

BB (%MV) 2.0%

B (%MV) 1.5%

Systematic Risk Thresholds² “Tail” Risk Thresholds³

Tracking Error Threshold: 350 bps¹

Curve/Currency/Spread

320 bps

Country/Industry/Issuer

140 bps


Integrating Risk Management and

Portfolio Management

At Prudential Fixed Income, risk

management and portfolio management are processes that are tightly integrated.

All of our strategies have an associated

risk budget, which guides the allocation of

risk capacity across available investment

opportunities. We develop our risk budgets with fairly tight thresholds, with

the understanding that these thresholds

will frequently be “tested” by portfolio

managers in the course of attempting to

uncover market opportunities. Our objective is not to impose absolute limits,

but rather to set thresholds at a level that

promote frequent and active dialogue

between the portfolio management and

risk management teams. We believe this approach—designed to integrate daily risk

awareness and management into the

search for alpha-generating

opportunities—permits us to most

efficiently capture those opportunities while remaining within manageable risk

tolerances.

includes both hard currency and local currency rates risk. That is, it can come from owning US-dollar

denominated sovereign bonds or from owning local currency bonds or swaps.

Principal Components—A Breakdown of Systematic Risk

While measuring and managing systematic risk by type of EM security

(spreads, Fx, rates) is useful, it does not tell the whole story because it

does not address the correlation of different types of risk. For example,

we know that EM spreads in certain country pairs are more correlated

than others, and also that EM spreads and EM currencies tend to be

positively correlated. On the other hand, historically, in highly-rated

countries, interest rate risk has been negatively correlated with spread

and currency risk in a majority of cases.

One way to capture these types of interactions is to use Principal

Component Analysis, or PCA, to measure and manage the systematic

risk. To conduct such an analysis, a historical data set is assembled

from the time series of returns (daily, weekly or monthly) of EM

investments grouped by country and risk category (spreads, Fx and

rates). This results in approximately 120 return series of EM country

spread sectors, currencies and rate sectors1 ranging over a full-

economic-cycle period of several years. The covariance matrix of this

dataset is then analyzed for its common risk factors using PCA, which is

a standard statistical analysis. The resulting principal components, or

PCs, are the risk factors that result, and are sorted by decreasing

importance. Each PC is a vector of simultaneous moves in all 120 EM

investments.

Luckily, an analysis of emerging markets debt reveals that the first few PCs capture most of the covariance. That

is, combinations of these PCs explain the majority of the typical covariation in all these individual investments. In

fact, as we will see, the first three principal components capture 70% to 80% of the overall systematic risk of hard

and local currency EM indices.

Principal Component 1: Systematic Directional Emerging Market Macro Risk

Although principal components fall out from a purely statistical analysis, it turns out that they often reflect how we

would intuitively expect EM investments to behave together in the market. For example, the first emerging market

PC, or PC1, is simply a vector of simultaneous moves in country spreads, currencies, and rates. It has two

intuitive properties that allow us to label it Systematic Directional EM Macro Risk or “EM Beta.” This is illustrated

in the following charts, which show the values of EM Beta or PC1 in three scatterplots for country spread,

currency and country rate sectors respectively, versus the total volatility of those same sectors. The figure shows

that the more volatile or riskier countries have higher PC1 values. In addition, all of the spread and Fx and most of

the rates elements have the same sign, which means in a PC1 move, they would all sell off or rally together. Thus

spreads, Fx, and interest rate exposures in risky countries all increase EM Beta and therefore the systematic risk

of the portfolio. PC1 is also positively correlated with traditional and intuitive systematic metrics such as VIX, Ted

spreads, commodity prices, and recently, European sovereign spreads.

However, a few countries‟ rate sectors show negative PC1s. These are the „safe‟ countries, including the US, but

also including countries such as Malaysia and Chile, which have high credit ratings and orthodox interest rate

1 Country spread sectors consist of the returns of the hard currency bonds from that country in the appropriate benchmark, after hedging out interest rate risk – thus leaving

only the spread-related return. Fx and Rates returns are similarly computed for each country from a local bond benchmark.


policies. In such countries, a sell-off in risk assets such as equities, spreads and currencies has historically been

accompanied by a rally in interest rates.1

PC 1

Systematic

Directional

EM Macro

Risk


Principal Component 2: Local Currency vs. Hard Currency Risk

The first PC, PC1, captures the Beta, or the simultaneous sell-off or rally of most emerging markets debt assets,

whether hard currency, Fx, or local currency.1 It is gratifying and exciting to find that the second PC, PC2, is

equally intuitive. It captures the opposite movement of local versus hard currency returns, because, as the

following chart illustrates, the PC2 components for the country spread sectors have signs opposite of those for Fx

and interest rates. Therefore PC2, which we label the “Local vs. Hard Currency” factor, describes the divergence

of emerging local markets returns from those of external debt spread markets. As we will see, PC2 is comparable

in importance to PC1 to the systematic risk of local bond investments.

Just as with PC1, the higher the overall volatility of the country sector, the higher PC2 tends to be. Many

emerging markets managers replace low-yielding hard currency bonds in their benchmarks with active positions

in local currency Fx and bonds, and PC2 measures the combined systematic contribution of these active

positions. Statistically, PC2 tends to be highly correlated with the euro, and with the local risk of Central Eastern

European, Middle Eastern and African (CEEMEA) local bond returns, indicating its importance in the environment

in which concern about Europe is driving a lot of risk aversion.

1 The “safer” the country, the more its rates assets are perceived to be safe havens and thus acquire this negative PC1 – which may be regarded as a hallmark of “flight-to-

quality” status. Of course, this historical behavior may not persist in the future if these rates markets become contaminated with “credit” concerns, as in peripheral Europe, or if inflationary concerns surface, in which case rising rates may force pro-cyclical policies that have the potential to damage growth. Recalibration would then be required to bring PCs back in line with market behavior.

(40)

(20)

0

20

40

60

80

100

0 100 200 300 400 (50)

0

50

100

150

200

0 100 200 300 400

Country Currency SectorsCountry Rate Sectors

PC

1

PC

1

Annual Volatility (bps)

0

50

100

150

200

250

300

0 100 200 300 400 500

Country Spread Sectors

PC

1


bpsbpsbps


Mexico Mexico Mexico

‘Safe’ Countries

PC 2

Local

Currency

vs.

Hard

Currency

Risk


(80)

(70)

(60)

(50)

(40)

(30)

(20)

(10)

0

0 100 200 300(50)

0

50

100

150

200

250

0 100 200 300 400

Country Spread SectorsCountry Currency SectorsCountry Rates Sectors


bps

Annual Volatility (bps) Annual Volatility (bps)

bps bps

PC

2

PC

2

PC

2

(20)

0

20

40

60

80

100

120

140

0 100 200 300 400

Mexico MexicoMexico


Principal Component 3 and Higher

The third principal component has a less obvious intuitive interpretation than the first two, but it appears to be

related to a systematic move up or down in local rates markets, while being less coherent and harder to interpret

with respect to Fx and spread sectors. It is also much less important risk-wise than the first two, in that it only

explains a small incremental additional percentage (typically 3-4%) of the overall risk, whereas the first two PCs

explain over half of all EM risk. The fourth and higher order PCs are even less important, harder to interpret, and

less robust―in that small changes to the dataset and methodology tend to alter or reorder them substantially. We

therefore capture them as part of our overall tracking error measurement rather than tracking them individually.

Estimates of country tracking errors/PCs are subject to drift as credits migrate and currency and rate volatilities

might not be stationary. Hence periodic recalibrations and implied ratings-based reassessments will be required.

Principal Components Explain the Majority of Systematic Risk

How much of the overall risk of EM investments is explained by the first three principal components? One way to

judge this is to measure what fraction of the overall covariance of the country sectors is explained by the first

three PCs. This number lies between 55% and 60%, suggesting that while these three PCs capture a substantial

amount of systematic risk, there is still a large residual of idiosyncratic country and asset-specific risk that

remains.

However, when spread, currency, and rate instruments of various countries are combined into an index or a well-

diversified portfolio, country specific and idiosyncratic risks tend to cancel out—the effect of diversification.1 Thus

when a portfolio consists of some combination of hard currency spreads, local currency, and local bond rates risk,

one should expect the bulk of the portfolio’s market risk to be explained by the first two or three PCs over the

historical period used to compute them.

How well then would these PCs have represented actual losses in a portfolio during specific market sell-offs? To

answer this question, we chose a recent period when EM assets sold off—May 2010―and asked the question,

How well would controlling for the systematic risks have protected a portfolio during that particular selloff? We

measured the PC1 and PC2 for an actual typical portfolio at the start of the sell-off period, and calculated the

short position (or underweight) in two specific benchmarks—the EMBI Global Diversified and the GBI—that would

have been required to match and hedge out the active PC1 and PC2 risks.2

In the following chart, we show the actual active return for a typical sample portfolio, which was -119 bps in May

2010, along with the “PC Hedged” return of -6 bps. This shows that using PC1 and PC2 to hedge was quite

effective. We have tested this for multiple portfolios in this and other market downturns, and the PCs continue to

provide effective measures of downside during such market downturns, validating the out-of-sample performance

of the risk measures.

1 For the purposes of this exercise, the US interest rate risk of the JPMorgan EMBI Global Diversified Index, the most commonly used hard currency benchmark, and the GBI

Index, the most common local bond market index, was “hedged out” by examining only the spread-related returns of the index. 2 Since we knew the PC1 and PC2 for the portfolio and the two benchmarks at the beginning of May 2012, this was a matter of solving a system of two equations in two

variables and two unknowns


Hedging

Systematic

Risk1 with PCs:

Practical

Market Test

(As of May 2010)

Source:Prudential Fixed Income.

Breaking Down Active Risks in a Sample Portfolio

To summarize, let us examine the actual risk breakdown of a sample EM portfolio‟s active positions (net of its

benchmark risks). These are illustrated in the following chart. One way the risk system can and should break

down the total active risk is in terms of asset class, as is shown on the left side of the chart―namely by risk

category. Thus, this particular portfolio has active spread risk arising from its hard currency sovereign, quasi-

sovereign, and corporate bonds totaling to 154 bps. Further, it has active Fx positions that total 102 bps of

currency risk, and active local rates positions accounting for another 30 bps or risk. However, to understand how

these three risks interact, one must turn one‟s attention to the right side of the chart, where you see the

systematic risks of the portfolio, expressed in terms of the first three PCs. The portfolio has 204 bps of PC1 or EM

Beta risk, arising from all three asset classes. This indicates that it is long beta to the tune of 204 bps. In addition,

it has -53 bps of PC2, showing that it is long local-markets vs. hard-currency risk. Finally, it has -22 bps of PC3.

These three PCs comprise a total of 211 bps in systematic risk. Further combined with 103 bps of idiosyncratic

risk, this totals to 235 bps of total risk in the portfolio.2

Portfolio Country

Stress Test3,4

As of March 17, 2011


Idiosyncratic Risk

Non-systematic or tail risks due to country, industry, and issuer exposures generally have negatively skewed

distributions. Because returns from these items—including instrument-specific returns—are not adequately

described by mean and variance, we supplement tracking error measures with custom stress tests and risk

thresholds to monitor and limit downside risk.

1 Active PC1, and PC2 neutralized using EMBI Global Diversified and GBI Index shorts. 2 The PC risks are orthogonal, so to get the total systematic risk one would take the square root of the sum of the first three squared PCs. The total risk, in turn, is the

square root of the sum of squares of the systematic and individual country/issuer risk. 3 Tracking Error (TE): Measures the expected annual variation (tracking error) of the portfolio’s return versus its benchmark due to active positioning. 4 Principal Components (PC): While tracking error reflects the total return volatility of the portfolio versus its benchmark, principal components provide a decomposition of

the total risk into independent "scenarios" or "factors" of risk.

Principal Components

Active Return

PC HedgedActive

Return

Percent Explained

by PCsPortfolio/Benchmark PC1 PC2 PC3

Sample Portfolio 201 32 -19 -119 -6 95.0%

Actual Portfolio active returns in May 2010

Returns of PC Hedged Portfolios without EM PC1, PC2, and PC3

By Risk Category

Spread

Currency

Rates

By Principal Component

EM PC1 (“Beta”)

EM PC2 (“Local”)

EM PC3

Total

Tracking Error154

102

30

Systematic

Risk

211

Individual

Country/Issuer

103

235

204

53

-22


The first of the exposures is country concentration, in which a two-fold risk approach is warranted. First, a screen

simulating a stress event is used to gauge a portfolio‟s risk exposure in an extreme environment, as is illustrated

below. Each country‟s spread, currency, and interest rate exposure is shocked in a manner that represents a

multiple standard deviation sell-off, which is then compared to a threshold level determined by the portfolio‟s risk

budget. The approach is also supplemented by a nominal market value threshold that is applied for low-liquidity

countries.

Portfolio Country

Stress Test Example

(As of March 17, 2011)


For purposes of screening for industry and issuer concentration, a market implied rating methodology is used,

where the quality assigned to each corporate issue is determined by its Option Adjusted Spread (OAS) and

maturity. Once this is calculated, we can then manage industry concentration via the use of a stress test in which

the portfolio‟s net exposure to a benchmark sector is shocked by an instantaneous spread widening. This again

represents multiple standard deviations, which range from 23 bps for short maturity, highest quality exposures in

industries exhibiting a low degree of correlation to over 2000 bps for low quality issues in highly correlated

industries. These exposures are calculated for each issue, then aggregated and compared to benchmark

exposures.

Industry Exposure Is Monitored Daily Through Stress Testing


Issuer risk is determined by aggregating each issuer‟s relative exposure to that permitted for every issuer. Riskier

exposures, such as subordinated bonds or capital securities, are then appropriately capped at lower

concentrations which we feel is superior to using simple market value or duration contribution thresholds.

Sample Portfolio Total Spread Currency Rates

Argentina -103 -63 -21 -19

Brazil -46 27 -49 -24

Chile 10 18 -8 0

Columbia -33 -4 -15 -14

Portfolio Benchmark Gap Gap Shock

Corr Holdings Dur Con %MV OAS Price Dur Con %MV OAS Price Dur Con %MV Impact

Aerospace/Defense M 3 0.0 0.2 219.6 104.4 0.0 0.2 120.9 111.6 0.0 -0.1 0.1

AAA/AA M 0.0 0.1 67.0 111.3 0.0 -0.1 0.3

A M 1 0.0 0.0 143.0 105.9 0.0 0.1 125.2 111.9 0.0 -0.1 0.8

BBB M 2 0.0 0.1 235.2 104.1 0.0 0.0 228.5 111.5 0.0 0.1 -1.1

BB M 0.0 0.0 306.0 110.1 0.0 0.0 0.1

Airline H 9 0.0 0.5 269.0 108.3 0.0 0.1 334.6 108.3 0.0 0.4 -6.3

BBB H 5 0.0 0.4 251.7 108.0 0.0 0.0 233.7 107.3 0.0 0.4 -5.2

BB H 4 0.0 0.1 339.7 109.8 0.0 0.0 372.5 110.4 0.0 0.1 -1.4

B H 0.0 0.0 580.0 105.0 0.0 0.0 0.2

CCC H 0.0 0.0 697.0 107.5 0.0 0.0 0.2

Automotive H 3 0.0 0.1 175.4 104.9 0.0 0.2 109.3 107.6 0.0 0.0 0.3

AAA/AA H 0.0 0.1 64.6 101.8 0.0 -0.1 0.2

Representative Broad Market PortfolioMarch 31, 2012

Illustrates potential impact of spread widening scenario applied to each industry, based upon our individual issue market-implied quality rating and the degree of correlation amongst issuers in that industry


Portfolios Are Screened for Significant Over/ Underweight Issuer Exposure to Limit Tail Risk


Structure Coupon MaturityPort

%MVBmk

%MVGap%MV

Threshold%

Market Implied Rating

OAS Price OAD Ratio

Bank 0.69 0.51 0.18 Ba2 554.7 94.74 3.4 1.10

SUB 5.42 03/15/2017 0.15 0.01 0.14 0.10 B2 672 90.22 4.5 1.43

SENIOR 4.50 04/01/2015 0.13 0.02 0.12 0.13 Ba3 517 96.50 3.0 0.88

SENIOR 3.70 09/01/2015 0.12 0.01 0.11 0.15 Ba2 521 93.04 3.4 0.76

SUB 6.05 05/16/2016 0.06 0.01 0.04 0.10 B2 680 94.26 3.9 0.45

SENIOR 6.00 09/01/2017 0.07 0.01 0.06 0.13 Ba3 542 97.66 4.8 0.41

SENIOR 5.80 06/07/2012 0.06 0.00 0.06 0.37 A3 515 100.21 0.4 0.15

SENIOR 5.45 02/05/2013 0.05 0.02 0.04 0.38 Baa1 451 100.73 1.1 0.10

SENIOR 6.05 08/15/2012 0.03 0.00 0.03 0.37 A3 355 101.44 0.6 0.08

SUB 6.00 10/15/2036 0.02 0.01 0.01 0.15 Ba2 487 83.17 11.4 0.07

SENIOR 5.63 07/01/2020 0.00 0.02 -0.02 0.13 Ba3 512 92.77 6.6 -0.15

SENIOR 6.40 08/28/2017 0.00 0.02 -0.02 0.11 B1 615 96.16 4.7 -0.16

SENIOR 7.38 05/15/2014 0.00 0.02 -0.02 0.13 Ba3 523 103.89 2.2 -0.16

SENIOR 5.75 12/01/2017 0.00 0.02 -0.02 0.13 Ba3 564 95.01 5.0 -0.17

SENIOR 5.65 05/01/2018 0.00 0.03 -0.03 0.13 Ba3 540 95.01 5.3 -0.19

SENIOR 6.88 04/25/2018 0.00 0.04 -0.04 0.12 B1 605 97.98 5.1 -0.32

Ratio Calculation0.12/0.13=0.88

Issuer thresholds based upon our individual issue market-implied quality rating and portfolio risk budget

Total ratio for issuer is the

sum of the individual

issuer ratios.

Total ratios above 1.00

signal review by risk

management.

Sample Portfolio


In Conclusion

The emerging markets debt asset class has historically undergone many crises, and understanding its

underlying risks is essential for any investor in hard currency sovereign and local market debt.

A careful risk classification and budgeting process is helpful in separately budgeting for systematic risks,

including spread, currency and interest rate risks, and idiosyncratic or „tail‟ risks, stemming from blow ups in

countries, industries, and individual issuers.

Principal components analysis allows systematic risk to be isolated into its most typical components, capturing

the common dimensions of emerging market sell offs across credit, local bond and currency markets. The first

three principal components describe 70% to 80% of all the risk in commonly used emerging market

benchmarks.

Country and industry stress tests combined with single issuer limits can be used to limit „tail‟ risks and protect

portfolios from individual country and industry crises, mitigating the impact of corporate defaults.

Notice

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