MFE 230G Equity & Currency Markets - PowerPoint PPT Presentation

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MFE 230G Equity & Currency Markets

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  1. MFE 230GEquity&CurrencyMarkets Michael Melvin Head of Currency Research Barclays Global Investors 415-908-7635

  2. LECTURE 1: Section 1 Overview of the FX Market

  3. 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

  4. World’s Largest Financial Market(BIS Triennial Survey)

  5. 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

  6. Who Dominates Market Making?(Euromoney FX Poll)

  7. LECTURE 1: Section 2 The Case for Active Currency Management

  8. 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

  9. 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

  10. Market Structure of Participants: shares of total FX volume

  11. 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

  12. 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

  13. 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

  14. LECTURE 1: Section 3 FX Market Microstructure

  15. 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

  16. 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

  17. 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

  18. 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

  19. Electronic Brokers: How Big?

  20. Electronic Brokers: Who has liquidity on each name?

  21. Electronic Brokers: Transparency

  22. Electronic Brokers: Liquidity

  23. LECTURE 1: Section 4 Temporal Patterns of FX Trading

  24. 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

  25. 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 maylead to misleading characterizations of trading intensity.

  26. 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

  27. Explaining….. More trading at launch

  28. Explaining….. Determinants of activity • Galati and Melvin (BIS, 2004) provide an analysis of the BISsurvey 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

  29. 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 λ For 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

  30. 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

  31. 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

  32. 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 • VariableCoefficientP-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)

  33. 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.

  34. 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: VariableCoefficientP-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= 0.674 Q(10) = 6.9035 (0.228)

  35. Explaining….. Intradaily • Determinants of Intradaily Trading Activity: • 1) Time of Day • 2) Stop-Loss Orders • 3) Trade Persistence

  36. 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.

  37. Explaining….. Intradaily

  38. 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

  39. 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.

  40. 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.

  41. 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.

  42. Explaining….. Intradaily We estimate the following for each month of our sample: