Currency Trading

Is downside risk the critical driver of investor asset valuation? In the January 2013 version of their paper entitled “Conditional Risk Premia in Currency Markets and Other Asset Classes”, Martin Lettau, Matteo Maggiori and Michael Weber explore the ability of a simple downside risk capital asset pricing model (DR-CAPM) to explain and predict asset returns. Their approach captures the idea that downside risk aversion makes investors view assets with high beta during bad market conditions as particularly risky. For all asset classes (but focusing on currencies), they define bad market conditions as months when the excess return on the broad value-weighted U.S. stock market is less than 1.0 standard deviation below its sample period average. To test DR-CAPM on currencies, they rank a sample of 53 currencies by interest rates into six portfolios, excluding for some analyses those currencies in highest interest rate portfolio with annual inflation at least 10% higher than contemporaneous U.S. inflation. They calculate the monthly return for each currency as the sum of its excess interest rate relative to the dollar and its change in value relative to the dollar. They then calculate overall and downside betas relative to the U.S. stock market based on the full sample. They extend tests of DR-CAPM to six portfolios of U.S. stocks sorted by size and book-to-market ratio, five portfolios of commodities sorted by futures premium and six portfolios of government bonds sorted by probability of default, and to multi-asset class combinations. They also compare DR-CAPM to optimal models based on principal component analysis within and across asset classes. Using monthly prices and characteristics for currencies and U.S. stocks during January 1974 through March 2010, for commodities during January 1974 through December 2008 and for government bonds during January 1995 through March 2010, they find that: Keep Reading

Do time series carry (intrinsic value) and time series momentum (intrinsic momentum) strategies work across asset classes? What drives their returns, and how do they interact? In the January 2013 very preliminary version of their paper entitled “The Returns to Carry and Momentum Strategies: Business Cycles, Hedge Fund Capital and Limits to Arbitrage”, Jan Danilo Ahmerkamp and James Grant examine intrinsic value strategy and intrinsic momentum strategy returns for 55 worldwide futures contract series spanning equities, bonds, currencies, commodities and metals, including the effects of business cycle/economic conditions and institutional ownership. They study futures (rather than spot/cash) markets to minimize trading frictions and avoid shorting constraints. They calculate futures contract returns relative to the nearest-to-maturity futures contract (not spot/cash market) price. The momentum signal is lagged 12-month cumulative raw return. The carry (value) signal is the lagged 12-month average normalized price difference between second nearest-to-maturity and nearest contracts. They test strategies that are each month long (short) contracts with positive (negative) value or momentum signals. They also test a combination strategy that is long (short) contracts with both value and momentum signals positive (negative). For comparability of assets, they weight contract series within multi-asset portfolios by inverse volatility, estimated as the average absolute value of daily returns over the past three months. Their benchmark is a long-only portfolio of all contracts weighted by inverse volatility. Using daily settlement prices for the nearest and second nearest futures contracts of the 55 series (10 equities, 12 bonds, 17 commodities, nine currencies and seven metals) as available during 1980 through 2012, they find that: Keep Reading

In her 2012 book A Trader’s First Book on Commodities: An Introduction to The World’s Fastest Growing Market (2nd Edition), author Carley Garner hopes to convey “the realization that anything is possible in the commodity markets. Never say ‘never’ — if you do, you will eventually be proven wrong. Additionally, trading the markets is an art, not a science. Unfortunately, there are no black-and-white answers, nor are there fool-proof strategies — but that does not mean that there aren’t opportunities.” Her further hope is that “this book is the first step in your journey toward victory in the challenging, yet potentially rewarding, commodity markets.” Some notable points from the book are: Keep Reading

Does hedging a currency carry trade (long currencies with high interest rates and short currencies with low interest rates) to suppress its volatility enhance performance? In their December 2012 working paper entitled “Carry Trade and Systemic Risk: Why are FX Options So Cheap?”, Ricardo Caballero and Joseph Doyle investigate the profitability of an equally weighted currency carry trade and the effects of hedging associated positions with currency exchange options. The hedged version matches short (long) exchange rate positions with at-the-money call (put) exchange rate options. Using monthly maturity date-matched VIX/VIX futures prices, spot/forward exchange rates for 67 currencies in U.S. dollars and one-month at-the-money option prices for 22 of these currencies (estimated from implied volatilities) during March 2004 (when VIX futures start trading) through August 2012 (96 monthly observations), they find that: Keep Reading

Does mean-variance optimization enhance the performance of currency carry trades (long currencies with high interest rates and short currencies with low interest rates)? In their November 2012 paper entitled “On the Risk and Return of the Carry Trade”, Fabian Ackermann, Walt Pohl and Karl Schmedders compare a dynamic mean-variance optimal carry trade strategy to naive ones. Specifically, they consider a series of monthly investments that are long (short) those of the following currencies with the highest (lowest) associated interest rates: U.S. dollar (base currency), Swiss franc, Euro, Japanese yen, British pound, Australian dollar, Canadian dollar, Norwegian krone, Swedish krona, Singapore dollar and New Zealand dollar. For monthly mean-variance optimization, they estimate currency correlations based on the last 250 days (one year) of daily data and set an annual excess return target of 5% (relative to the risk-free rate), the approximate excess return on the S&P 500 Index over the same period. For naive portfolios, they consider 1 long/1 short currencies, 3 long/3 short equally weighted currencies and the S&P 500 Index total return. Using daily currency values and monthly S&P 500 Index data during January 1989 through June 2012 (using the first year for initial optimization), they find that: Keep Reading

Do currency exchange returns exhibit reliable daily patterns? In their March 2012 paper entitled “Intraday Patterns in FX Returns and Order Flow”, Francis Breedon and Angelo Ranaldo investigate currency exchange returns during local trading hours and the balance of the day. They analyze six exchange rates: euro-U.S. dollar; U.S. dollar-yen; Great Britain pound-U.S. dollar; euro-yen; U.S. dollar-Swiss franc; and, Australian dollar-U.S. dollar. Using hourly currency exchange spot bid, ask and execution prices and order flow data for January 1997 through May 2007 (mostly excluding weekends but including holidays) converted to New York time, they find that: Keep Reading

Whenever the dollar persistently appreciates or depreciates versus some other currency, experts theorize. A depreciating dollar is good because U.S. exports boom and domestic employment rises. Or, a depreciating dollar is bad because capital flees the U.S., and import prices (especially for crude oil) spur inflation. Are there reliable and exploitable relationships between the euro/dollar and yen/dollar exchange rates and the U.S. stock market? To investigate, we apply regressions and rankings to characterize interactions between each exchange rate and the stock market. Using daily data for euro/dollar and yen/dollar exchange rates and the S&P 500 Index over the period January 2000 through most of June 2011 (about 12.5 years), we find that: Keep Reading

Are there ways to enhance the currency carry trade (long currencies offering high interest rates and short those offering low rates)? In the May 2012 version of their paper entitled “Average Variance, Average Correlation and Currency Returns”, Gino Cenedese, Lucio Sarno and Ilias Tsiakas investigate the ability of components of the currency exchange market risk (variance of the average return for all exchange rates) to predict carry trade returns. Their baseline carry trade portfolio involves U.S. dollar nominal exchange rates, rebalanced monthly. They decompose the market variance into two components: average variance of individual exchange rate returns, and average correlation of exchange rate returns. They examine the effects of changes in these risk components on the entire future distribution of currency trade returns (via quantile breakdowns), focusing on the large losses in the left tail and large gains in the right tail. Using daily spot and forward exchange rates for 33 currencies relative to the U.S. dollar as available during 1976 through February 2009 (15 active exchange rates at the beginning and 22 at the end), they find that: Keep Reading

How attractive can currency trading be after optimizing across several anomalies? In the November 2011 version of their paper entitled “Beyond the Carry Trade: Optimal Currency Portfolios”, Pedro Barroso and Pedro Santa-Clara examine the performance of utility-maximized currency strategies designed to exploit interest rate variables, momentum, long-term reversal, current account and real exchange rate during the floating exchange rate era. They also investigate whether such currency strategies are valuable to investors holding portfolios of equities and bonds. Their benchmark portfolio consists of $1 invested in the U.S. risk-free rate and $1 risked in a hedged carry trade (long all currencies yielding more than the U.S. dollar and short all others, with long and short sides equal and equal weighting across currencies within each side). They assume a power law utility function with constant level of risk aversion to specify optimal currency weightings. They perform out-of-sample testing based on inception-to-date regressions executed annually to specify optimal portfolios for the next year, commencing 240 months into the sample. Using spot and one-month forward exchange rates and data on current accounts and inflation as available for 27 developed economies during November 1960 through September 2010 (a total of 7,197 monthly currency returns involving 13 to 21 currencies per year), they find that: Keep Reading

How do relevant electronic social networks affect individual investing? In their March 2012 paper entitled “Facebook Finance: How Social Interaction Propagates Active Investing”, Rawley Heimer and David Simon investigate the propagation of active investing strategies within a Facebook-like social network of retail foreign exchange traders. Registered users of this free network (who must have a qualified foreign exchange broker account) have access to: (1) an indicator of the aggregate positions of the entire network in specific currency pairs; and, (2) a real-time view of the trading activity of mutually accepted “friends.” The network receives information about user trades instantly from qualified brokers. Using a complete record of activities within this network involving more than 5,500 foreign exchange traders, two million time-stamped trades and 140,000 messages and friendships mostly between February 2009 and December 2010, they find that: Keep Reading