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MFE 230GEquity & Currency Markets

Michael MelvinHead of Currency ResearchBarclays Global Investors

michael.melvin@barclaysglobal.com415-908-7635

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MFE 230G: Equity &Currency Markets (Currency Markets Portion)

Haas School of Business, UC Berkeley, Fall 2008

Instructor: Michael Melvin Office Hours: Before class Tel: 415 908 7635 Email: michael.melvin@barclaysglobal.com Course Description This is the second half of 230G and is dedicated to currency markets: institutions, participants, determination of spot and forward exchange rates, and constructing a model for active currency investing. Requirements For the currency markets half of the course there will be a weekly quiz, a homework problem set, and a final exam. The problem set needs to be handed in individually, but I encourage discussion among students. Readings The required text for the currency markets half is The Microstructure Approach to Exchange Rates, Richard Lyons, MIT Press, 2001 (denoted “MAER” in the following schedule). Required readings that aren’t in the text are collected in the reading packet. You are expected to do the reading before the corresponding lecture. Lectures will be based upon the instructor’s notes (you will receive PowerPoint slides), with the readings providing detailed background information.

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Session 1: Tuesday, Sept 9: The FX market: organization and players MAER chapter 3. BIS, Triennial Central Bank Survey, December 2007 (just read for the

“what and where” of currency trading, don’t worry about the small details). Brzeszczynski & Melvin, “Explaining Trading Volume in the Euro,” International Journal of Finance and Economics 25-34, 2006.

Session 2: Friday, Sept 12: Exchange Rate Models

MAER chapters 1 & 6 Taylor & Taylor, “The Purchasing Power Parity Debate,” Journal of Economic Perspectives, 2004.

Alquist & Chinn, “Conventional and Unconventional Approaches to Exchange Rate Modeling and Assessment,” International Journal of Finance and Economics, 2008.

Session 3: Tuesday, Sept 23: Forecasting currency returns MAER chapter 7, sections 7.1, 7.2, 7.4.

Galati & Heath, “What Drives the Growth in FX Activity?” BIS Quarterly Review, 2007. Cheung & Chinn, “Currency Traders and Exchange Rate Dynamics,” Journal of International Money & Finance, 2001.

Session 4: Tuesday, Sept 30: Active Currency Investing

Aston, Bird, & Middleton, “Bank of America Currency Indices” Pojarliev & Levich, “Do Professional Currency Managers Beat the Benchmark?” forthcoming Journal of Portfolio Management.

Final Exam over both halves of MFE 230G: Tuesday, October 7

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LECTURE 1: Section 1

Overview of the FX Market

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What is foreign exchange and where is the market?

• Foreign exchange refers to bank deposits denominated in foreign currency and banknotes

• Prices are exchange rates expressed like EURUSD– EUR is “base” currency, USD is “term” currency– This is the dollar price of 1 euro, like 1.4650

• Global market with 24-hour trading

• No physical location, telephone and electronic trading– A decentralized dealership market

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World’s Largest Financial Market(BIS Triennial Survey)

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What is traded & where?(BIS Triennial Survey)

• London the biggest market (34%), followed by New York (17%) and Zurich, Tokyo, & Singapore (6%) • Still a USD world

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Who Dominates Market Making?(Euromoney FX Poll)

Bank Market ShareDeutsche Bank 22%

UBS 16%

Barclays Capital 9%

Citi 7%

RBS 7%

JPMorgan 4%

HSBC 4%

Lehman Bros 4%

Goldman Sachs 3%

Morgan Stanley 3%

Bank of America 2%

Dresdner Kleinwort 2%

BNP Paribas 2%

Credit Suisse 2%

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LECTURE 1: Section 2

The Case for Active Currency Management

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Why Alpha Exists in Currency Markets: Participants

• Profit Oriented Participants – Look to profit from exchange rate changes

• Currency overlay managers• Hedge funds

• Liquidity Oriented Participants– Access market to fund international transactions

• Tourists• Corporates financing payables & receivables or hedging• Central banks intervening, providing liquidity, or managing volatility as a policy tool• Global investors in fixed income & equities (hedging or not)

• Dealers are 3rd party intermediaries between liquidity providers and liquidity takers

• If the other two parties did not trade or could trade directly, there would be no dealers– Dealers charge bid-ask spread for providing intermediation services

• Dealers manage risk by passing their positions to others– Profit-oriented traders will take their positions for a price

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Where does the alpha come from?

• Liquidity-oriented pay a premium to induce profit-oriented to trade– Generates systematic returns for profit-oriented who provide liquidity

• Successful profit-oriented firms:– are good forecasters– anticipate flows generated by liquidity takers

• central bank policy making & associated trades• global investor trades in equity & fixed income

– exploit information asymmetries & have trade execution advantages

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Market Structure of Participants: shares of total FX volume

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1992 1995 1998 2001 2004 2007

non-financial customersother financial institutionsreporting dealers

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Market Structure of Participants: Detail

• Reporting dealers– Interbank, intermediary flows– Down from 1992,69% to 2007, 43%

• Other financial institutions– Small non-dealer banks, mutual funds, pension funds, hedge funds,

currency overlay funds, money market funds, insurance cos, etc– Up from 1992, 13% to 2007, 40%

• Non-financial customers– Corporates and governments– Steady from 1992, 18% to 2007, 17%

• Total market size up from 1992, $840B to 2007, $3,081B

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How big is the liquidity-oriented share of the market?

• Must make informed guess, as an OTC market with no marketwide data

• Lower bound: the 17% of non-financial customers• + some fraction of the other financial institutions

– DB estimates that 40% of FX turnover can be attributed to buying/selling goods, services, or other financial assets ($1,200B a day)

• implies 23% attributed to non-dealer financial customers• What about other 60% of turnover?

– Includes loans, currency hedging, and speculative trades (profit oriented traders)

– DB estimates that about half of this is loan and hedging-related and half profit-oriented speculative trading

• Implies 70% of FX turnover is liquidity-oriented• Appears to be consistent opportunity for profit-oriented

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Ancillary evidence

• Chicago Mercantile Exchange data on FX futures– Non-commercial (profit-oriented speculators) earn profits over time– Commercial (liquidity-oriented hedgers) lose money on positions

• Of course, commercial hedgers would not evaluate P&L in this manner• Reserve Bank of Australia study of speculators in FX futures

– Non-commercial (profit-oriented speculators) earned profits on every currency over 10-year sample period

– Commercial (liquidity-oriented hedgers) lost money on every currency – Conclude that profit-oriented traders earn a risk premium for providing liquidity

and earn positive returns from superior forecasting ability• Central bank policy of “leaning against the wind” generates losses from FX

intervention – Creating profit opportunities for others

• There are consistent opportunities for alpha in FX– Participants have different motivations for trading, different sources of

information, and different ways of processing information

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LECTURE 1: Section 3

FX Market Microstructure

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Market Structure

• Decentralized multiple-dealership market– Market fragmented

• Low trade transparency– Only parties to trade no what was traded in most transactions– Electronic platforms allow for more info than prior to 1990s

• See streaming prices and can infer trade activity• No size info

• Voice brokers making interbank comeback due to algo trading on E-platforms– Banks don’t want their trades to trigger algo trades

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Participants

Dealers • Marketmakers: provide liquidity & two-way prices

– Interbank & customer trades

Customers• Central bank, non-bank financial institutions, smaller banks,

corporates

Brokers• Intermediate trades

– Historically just interbank, but “democratization” via electronic platforms– An alternative to “direct dealing”

• Anonymity provided

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Dealers receive info from customer orders

• Order flow contains information– Big banks with big client bases have advantage over others

• Infer positioning• See initial large orders that will have market impact

• Private info flows between trading direct-dealing counterparties– Imagine you receive central bank intervention order

• Brokered trades are visible to platform participants– See price of a completed deal, so can infer whether buy or sell

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Transparency of Order Flow

• Pre-trade vs post-trade info– Direct-dealing has no pre-trade transparency except for counterparty

interest– Don’t know what others quote without checking

• Price vs quantity info– Marketwide info on price only via E-broker sites– No quantity info anywhere

• Public vs dealer info– In opaque market order flow not shared widely so info may be

impounded in price more slowly– Info asymmetry helps dealers manage risk of large positions

• Order flow traditionally ignored in exchange rate models– Tradition of “macro” models, but order flow belongs more to the “micro”

world

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Electronic Brokers: How Big?

ADV Estimates J uly 2007 ($ billion)

1

1

8

21

52

100

185

- 20 40 60 80 100 120 140 160 180 200

FXall

FXMS

Lava

Hotspot

Currenex

Reuters

EBS

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Electronic Brokers: Who has liquidity on each name?

-

20.0

40.0

60.0

80.0

100.0

120.0

EURUSD GBPUSD EURGBP USDJ PY EURJ PY USDCHF EURCHF USDCAD AUDUSD NZDUSD

EBS Reuters Currenex Hotspot Lava

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Electronic Brokers: TransparencyNo depth of book. Instead there are “regular” amount, “best regular” bid/offer and “X-pips defined by EBS

Inside quote will display up to the “regular” amount defined for each currency pair“Best regular” bid/offer rates indicate the worst price to aggress for “regular” amountHowever, “best regular” bid/offer will be blank if there is not “regular” amount within “X-pips” of the inside quote

Quotes are updated based on randomized timeslices on a per second cycle.Report only the highest and lowest priced execution per each second cycleDoes not report the quantity tradedNo depth of book. Reuters provides “regular” amount, “aggregate” price, “standard” quantity and “switch off” threshold

Inside quote will display up to the “regular” amount defined for each currency pair.“Aggregate” price displays the worst price at which the “standard” quantity can be traded. “Standard” quantity is pre-defined for each currency pair and may not equal to the “regular” amount“Aggregate” price will be blank if the “standard” quantity can not be executed within the “switch off” threshold

Quote refreshes every 500 millisecondsReport the last traded price without the amount informationFull depth of bookReal-time price updatesReport last traded price without the amount information

Full depth of bookReal-time price updates at the matching engineMarket data quotes updated at either 50ms or 250 ms intervalsReport last traded price without the quantity informationFull depth of bookReal-time price updatesReport executions without amount at end of day, no real-time reporting.

Lava

EBS

Reuters

Currenex

Hotspot

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Electronic Brokers: Liquidity

The recently introduced "Depth of Book View" provides traders with more visibility into the inventory available in the EBS Order Book. The Best Dealable Amount represents the inventory available at the Best Dealable Price. The Best Dealable +/ - 1 Amount represents the inventory available at the Best Dealable Price +/ - 1 pip increments.

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LECTURE 1: Section 4

Temporal Patterns of FX Trading

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Brzeszczynski & Melvin, Explaining Trading Volume in the Euro

• The purpose of this study is to introduce several stylized facts of the FX market– The academic purpose was to provide an early view of the pattern of

trading activity in the euro since its start.

• „From bird’s eye to microscope” approach– Data frequencies used:

- weekly- daily - intradaily (5-minutes intervals).

• The euro first appeared and began trading at the beginning of 1999

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Explaining….. Data

• The data are drawn from a major electronic brokerage platform for currency trading (Reuters).

• A record of every trade that occurred on the euro against the U.S. dollar over the period of: January 1, 1999 - October 7, 2003.

• While data exist for both the dollar value as well as number of trades, we focus on the latter variable as an indicator of trading activity.

• The dollar value includes the effect of exchange rate changes and such valuation effects may lead to misleading characterizations of trading intensity.

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Explaining….. Data

• Data have been converted to standard normal variates (we remove the mean in order to protect the vendor’s proprietary rights).

• Holidays and weekends removed.

• There is a „smile pattern” where trading activity was on a downward trend until early 2002, after which trading activity started an upward trend

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Explaining….. More trading at launch

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Explaining….. Determinants of activity

• Galati and Melvin (BIS, 2004) provide an analysis of the BIS survey data on global foreign exchange trading and conclude that in the early 2000s three factors appeared to contribute to rapid growth in foreign exchange trading:– Exchange rate trends that fostered “momentum-based” trading, – Interest-rate differentials that led to “carry-trades”– Growth of interest in currencies as an asset class alternative to equity

and fixed income

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Explaining….. momentum

• We model trends in the USD/EUR by applying the H-P filter to weekly data to create a smoothed exchange rate series.

– The Hodrick-Prescott Filter is a popular smoothing method to obtain an estimate of the long-term trend component of a series. It is a two-sided filter that computes the smoothed series s of series y by minimizing the variance of y around s, subject to a penalty that constrainst the second difference of s. The HP filter chooses s to minimize:

Where smoothness is controlled by penalty parameter λFor a smoother s, choose a larger λ

• Then the change in the log of the smoothed series is used as a determinant of trade activity

1 22

1 11 2

T T

t t t t t tt t

y s s s s s

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Explaining….. Carry trade

• In terms of trading activity, we would expect that changes in the interest rate differential would induce more carry-related trades

• the absolute value of the change in the interest differential between the ECB marginal lending facility rate and the Federal Reserve federal funds rate is used as an explanatory variable

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Explaining….. currencies as an asset class

• U.S. Treasury publishes data on the futures and options positions of large foreign exchange market participants at the weekly frequency.

• These are market participants with more than $50 billion in foreign exchange contracts on the last business day of any quarter during the previous year.

• The absolute difference of purchases minus sales of euros against dollars is used as proxy for positioning

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Explaining….. Weekly model• The number of trades per week were aggregated to create the dependent variable

of interest: Numtrades• Then the following equation was estimated:

• Where– Trend - the change in the log of the smoothed USD/EUR exchange rate estimated via the H-P

filter;– Intdiff - the absolute change in the interest differential between the federal funds and ECB

interest rates;– Positions - the absolute value of purchases minus sales of euros against dollars by big

market participants• Variable Coefficient P-value• Constant 14,405.8 (0.000)• Trend 1,558,603 (0.041)• Intdiff 1,496.1 (0.092)• Positions 0.019 (0.315)• AR1 0.732 (0.00)•  R2= 0.644• Q(10) = 13.047 (0.175)

t 0 1 t 2 t 3 t tNumtrades Trend Intdiff Positions u

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Explaining….. Daily activity

• Calendar effects– Market participants typically expect liquidity to be lower on Friday

than on other days. • This effect is due to aversion to opening positions prior to the weekend.• With two days of non-trading, any news that may occur over the weekend

cannot be met with a reaction. So position changes are not possible. As a result, we should expect trading volume to be lower on Fridays than on other days.

– In order to examine day-of-the-week effects, a dummy variable was created for each day of the week.

• For instance, MON is a dummy variable for Monday which is equal to 1 on Monday and zero otherwise. Similarly, the variables TUE, WED, THUR, and FRI are created.

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Explaining….. Daily activity• Central bank policy events

– Dummy variables FED and ECB are created that equal 1 on days when the Federal Reserve or the ECB change their target rates, respectively

The following model was estimated:

Variable Coefficient P-value

MON -22.202 (0.638)TUE 752.832 (0.000)WED 20.902 (0.671)THU -20.443 (0.681)FRI -497.751 (0.000)FED -116.159 (0.202)ECB 119.792 (0.209)R2= 0.674Q(10) = 6.9035 (0.228)

t 0 t 1 t 2 t 3 t 4 t

6 t 7 8 t 1 t

Numtrades MON TUE WED THUR FRIFED ECB Numtrades u

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Explaining….. Intradaily

• Determinants of Intradaily Trading Activity:

• 1) Time of Day

• 2) Stop-Loss Orders

• 3) Trade Persistence

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Explaining….. Intradaily

Time of Day

• Prior work on exchange rate microstructure has demonstrated that there is a regular pattern of activity in the foreign exchange market at the intradaily level.

• We explore this pattern with a sample of 5-minute frequency trade volume for the 2003 data in our data set, that is January-October, 2003.

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Explaining….. Intradaily

Intradaily Number of Trades per 5-Minute Interval for USD/EUR (standardized)January 2003

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 231 241 251 261 271 281

Time Interval

Stan

dard

ized

Num

ber o

f Tra

des

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Explaining….. Intradaily

• There are 288 5-minute intervals each day.

• The active period of trading starts around 8 a.m. London time (interval 92) and ends around 4 p.m. London time (interval 192).

• The two largest spikes occur following the period of 13:30 London (8:30 New York) when most U.S. macro news announcements are received and 15:00 London (10:00 New York) when many foreign exchange options expire and trading related to unwinding delta hedges occurs– There is also some U.S. macro news that occurs at 15:00 London

time

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Explaining….. Intradaily

• We employ two dummy variables to capture the slow period when trading is basically flat.

• Dum800 is a dummy that is set equal to 1 during the period of midnight to 8:00 London time and equals zero otherwise.

• Dum1600 is a dummy set equal to 1 during the period of 16:00 to midnight London time and is zero otherwise.

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Explaining….. Intradaily

Stop-Loss Orders• Recent research has attempted to link large exchange rate

changes or “price cascades” to the presence of stop-loss or take-profit orders. – orders that customers place with banks that will trigger purchases or

sales of currency once the exchange rate reaches a particular level– Osler (JF, 2005; JIMF, 2003) has shown that such orders tend to

cluster at round numbers or “big figures”• We examine the role of crossing round numbers in our high-

frequency data set on dollar/euro trades by creating a variable Round that is set equal to 1 in any 5-minute interval in which a round number is passed.

• For the EURUSD, that would be any time the exchange rate passes the “big figure” which is the exchange rate at two decimal points like 1.25 or 1.05.

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Explaining….. Intradaily

Trade Persistence

• The data on high-frequency trade volume are highly autocorrelated.

• If there is a very active market in the current 5-minute interval, there is likely to be very active trading in the next 5-minute interval.

• For this reason, we include a lagged value of trade volume as an explanatory variable.

• In addition, it is necessary to model the residuals to account for any remaining autocorrelation and transform the errors to white noise.

• We examine models for each month of our sample and fit a separate noise model to each month.

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Explaining….. Intradaily

We estimate the following for each month of our sample:

t 1 t 2 t 3 t 4 t 1 tTrades c Round Dum800 Dum1600 Trades Constant Round Dum800 Dum1600 Tradest-1 R2 Q(12) Obs Month January 3.036

(0.00) 2.668 (0.00)

-2.438 (0.00)

-2.936 (0.00)

0.854 (0.00)

0.668 12.93 (0.17)

6,718

February 4.371 (0.00)

2.136 (0.00)

-3.874 (0.00)

-4.278 (0.00)

0.815 (0.00)

0.660 11.74 (0.11)

6,144

March 3.382 (0.00)

0.804 (0.01)

-2.736 (0.00)

-3.320 (0.00)

0.872 (0.00)

0.721 10.11 (0.12)

6,528

April 0.640 (0.00)

1.452 (0.00)

-0.190 (0.15)

-0.702 (0.00)

0.962 (0.00)

0.704 13.40 (0.15)

6,720

May 0.668 (0.00)

1.205 (0.00)

-0.066 (0.00)

-0.712 (0.00)

0.954 (0.00)

0.713 9.148 (0.17)

6,720

June 1.519 (0.00)

0.725 (0.00)

-0.806 (0.00)

-1.570 (0.00)

0.923 (0.00)

0.708 7.721 (0.17)

6,528

July 1.550 (0.00)

1.131 (0.00)

-0.880 (0.00)

-1.595 (0.00)

0.916 (0.00)

0.677 7.051 (0.42)

7,008

August 0.742 (0.00)

0.569 (0.00)

-0.274 (0.00)

-0.801 (0.00)

-0.955 (0.00)

0.695 6.010 (0.65)

6,528

September 1.306 (0.00)

0.738 (0.00)

-0.662 (0.00)

-1.404 (0.00)

0.943 (0.00)

0.679 10.420 (0.17)

6,720

October 2.658 (0.00)

1.911 (0.00)

-1.798 (0.00)

-2.701 (0.00)

0.899 (0.00)

0.719 2.590 (0.76)

6,720