Currency Trading

Has crude oil turned into paper from an investment perspective? In their May 2013 paper entitled “Oil Prices, Exchange Rates and Asset Prices”, Marcel Fratzscher, Daniel Schneider and Ine Van Robays examine relationships between crude oil price and behaviors of other asset classes. Specifically, they relate spot West Texas Intermediate (WTI) crude oil price to: the U.S. dollar exchange rate versus a basket of developed market currencies; Dow Jones Industrial Average (DJIA) return; U.S. short-term interest rate; the S&P 500 options-implied volatility index (VIX); and, open interest in the NYMEX crude oil futures (as an indication of financialization of the oil market). They also test the response of crude oil price to economic news. Using daily data for these financial series during January 2001 through mid-October 2012, and contemporaneous U.S. economic news and associated expectations, they find that: Keep Reading

Do currency exchange rates exhibit short-term momentum? In the April 2013 version of their paper entitled “Is There Momentum or Reversal in Weekly Currency Returns?”, Ahmad Raza, Ben Marshall and Nuttawat Visaltanachoti investigate whether exchange rate movements over the past one to four weeks persist over the next one to four weeks. They test these 16 alternative strategies (four look-back intervals times four holding intervals) by each week buying (selling) the fifth of available currencies that have appreciated (depreciated) the most against the U.S. dollar. Using weekly and monthly spot and forward prices for 63 emerging and developed market currencies versus the U.S. dollar as available during October 1997 through December 2011, they find that: Keep Reading

Does adding a proxy for the currency carry trade among developed economies (long futures on three currencies with the highest interest rates and short futures on three currencies with the lowest interest rates) to a diversified portfolio improve its performance? To check, we add PowerShares DB G10 Currency Harvest (DBV) to the following mix of asset class proxies (the same used in “Simple Asset Class ETF Momentum Strategy”):

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

First, per the findings of “Asset Class Diversification Effectiveness Factors”, we measure the average monthly return for DBV and the average pairwise correlation of DBV monthly returns with the monthly returns of the above assets. Then, we compare cumulative returns and basic monthly return statistics for equally weighted (EW), monthly rebalanced portfolios with and without DBV. We ignore rebalancing frictions, which would be about the same for the alternative portfolios. Using adjusted monthly returns for DBV and the above nine asset class proxies from September 2006 (first return available for DBV) through April 2013 (79 monthly returns), we find that: Keep Reading

What works better for currency trading, technical or fundamental analysis? In their April 2013 working paper entitled “Exchange Rate Expectations of Chartists and Fundamentalists”, Christian Dick and Lukas Menkhoff compare the behavior and performance of technical analysts (chartists) and fundamental analysts (fundamentalists) based on monthly surveys of several hundred German professional dollar-euro exchange rate forecasters, in combination with respondent self-assessments regarding emphasis on technical and fundamental analysis. Forecasts are directional only (whether the dollar will depreciate, stay the same or appreciate versus the euro) at a six-month horizon. The authors examine three self-assessments (from 2004, 2007 and 2011) to classify forecasters as chartists (at least 40% weight to technical analysis), fundamentalists (at least 80% weight to fundamental analysis) or intermediates. Using responses from 396 survey respondents encompassing 33,861 monthly time-stamped forecasts and contemporaneous dollar-euro exchange rate data during January 1999 through September 2011 (153 months), they find that: Keep Reading

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