Currency Trading

Do changes in the dollar-euro exchange rate reliably interact with the U.S. stock market and gold? For example, do declines in the dollar relative to the euro indicate increases in the dollar value of hard assets? Are the interactions coincident or exploitably predictive? To investigate, we relate changes in the dollar-euro exchange rate to returns for U.S. stock indexes and spot gold. Using end-of-month and end-of-week values of the dollar-euro exchange rate, levels of the S&P 500 Index and Russell 2000 Index and spot prices for gold during January 1999 (limited by the exchange rate series) through October 2016, we find that: Keep Reading

Bitcoin is a currency based on cryptographic proof rather than traditional trust, with transactions taking place directly between users and recorded in a distributed public ledger. How wild is the exchange rate for this new form of currency? In their November 2016 paper entitled “A Statistical Risk Assessment of Bitcoin and Its Extreme Tail Behaviour”, Joerg Osterrieder and Julian Lorenz examine the recent distribution of daily Bitcoin-U.S. dollar exchange rate returns, with focus on tail risk metrics. They also compare Bitcoin exchange rate return statistics to those for G10 currencies versus the U.S. dollar. Using daily Bitcoin Baverage and G10 currency exchange rates relative to the U.S. dollar during September 2013 through September 2016, they find that: Keep Reading

Do global equity funds generate alpha after accounting for both equity and currency factors? In their October 2016 paper entitled “Global Equity Fund Performance Evaluation with Equity and Currency Style Factors”, David Gallagher, Graham Harman, Camille Schmidt and Geoff Warren measure the performance of global equity funds based on their quarterly holdings after adjusting for market return, six widely used equity factor returns and three widely used currency exchange factor returns. The six equity factors are size (market capitalization), value (average of book-to-market and cash flow-to-price ratios), momentum (return from 12 months ago to one month ago in local currency), investment (quarterly change in total assets), profitability (return-on-equity) and illiquidity (impact of trading). The three currency exchange factors are trend (3-month average exchange rate minus 12-month average exchange rate), carry (reflecting short-term interest rate differences) and value (based on deviation from purchasing power parity). They also test developed and emerging markets holdings of these funds separately. Using quarterly stock holding weights for 90 institutional global equity funds priced in U.S. dollars, and contemporaneous equity and currency exchange factor return data, during 2002 through 2012, they find that: Keep Reading

Does time series (intrinsic or absolute) momentum work across asset classes prior to the Great Moderation (secular decline in interest rates)? In their August 2016 paper entitled “Trend Following: Equity and Bond Crisis Alpha”, Carl Hamill, Sandy Rattray and Otto Van Hemert test several time series momentum portfolios as applied to groups of bonds, commodities, currencies and equity indexes as far back as 1960. They consider 10 developed country equity indexes, 11 developed country government bond series, 25 agricultural/energy/metal futures series and nine U.S. dollar currency exchange rate series. They calculate return momentum for each asset as the weighted sum of its past monthly returns (up to 11 months) divided by the normalized standard deviation of those monthly returns. They then divide each signal again by volatility and apply a gearing factor to specify a 10% annual volatility target for each holding. Within each of equity index, bond and currency groups, they weight components equally. Within commodities, they weight agriculture, energy and metal sectors equally after weighting individual commodities equally within each sector. They report strategy performance based on excess return, roughly equal to real (inflation-adjusted) return. They commence strategy performance analyses in 1960 to include an extreme bond bear market. Using monthly price series that dovetail futures/forwards from inception with preceding spot (cash) data as available starting as early as January 1950 and as late as April 1990, all through 2015, they find that: Keep Reading

Is global diversification within asset classes disappearing as worldwide economic and financial integration increases? In their August 2016 paper entitled “Globalization and Asset Returns”, Geert Bekaert, Campbell Harvey, Andrea Kiguel and Xiaozheng Wang examine whether economic and financial integration increases global comovement of country equity, bond and currency exchange market returns. They examine three measures of return comovement for each asset class: average pairwise correlation, average beta relative to the world market and average idiosyncratic volatility. They apply these measures separately to developed markets and emerging markets. Using monthly equity, bond and currency exchange market returns in U.S. dollars for 26 developed markets and 32 emerging markets as available from various inceptions through December 2014, they find that: Keep Reading

How do individual currency traders view their trading experience? In his June 2016 paper entitled “Retail FX Trader Survey Results”, Chris Davison reports results of an anonymous survey of retail currency traders asking 14 questions about the way they trade. He elicited participants via posts on two online currency trading forums: Forex Factory and MyFXbook. Using responses from 133 traders during late November 2015 through late April 2016, he finds that: Keep Reading

How well does technical trading work for spot currency exchange rates? In their April 2016 paper entitled “Technical Trading: Is it Still Beating the Foreign Exchange Market?”, Po-Hsuan Hsu, Mark Taylor and Zigan Wang test the effectiveness of a broad set of quantitative technical trading rules as applied to exchange rates of 30 currencies with the U.S. dollar over extended periods. They consider 21,195 distinct technical trading rules: 2,835 filter rules; 12,870 moving average rules; 1,890 support-resistance signals; 3,000 channel breakout rules; and, 600 oscillator rules. They employ a test methodology designed to account for data snooping in identifying reliably profitable trading rules. They focus on average return and Sharpe ratio for measuring rule effectiveness. They use empirical bid-ask spread data as available to estimate costs (averaging 0.045% one way for developed markets and 0.21% one way for emerging markets). They also test whether technical trading effectiveness weakens over time. Using daily U.S. dollar spot exchange rates and associated bid-ask spreads as available for nine developed market currencies and 21 emerging market currencies during January 1971 through mid-September 2015, they find that: Keep Reading

Do commodity price changes predict currency exchange rate fluctuations for commodity-exporting countries? In their March 2016 paper entitled “When the Walk is Not Random: Commodity Prices and Exchange Rates”, Emanuel Kohlscheen, Fernando Avalos  and Andreas Schrimpf analyze relationships between commodity prices and exporter exchange rates. They first construct daily commodity export price indexes tailored to 11 commodity-exporting countries (Australia, Brazil, Canada, Chile, Colombia, Malaysia, Mexico, Norway, Peru, Russia, South Africa), encompassing 83 commodities (26 metal, 36 agricultural, 11 livestock, 10 energy). They then relate index levels to daily currency exchange rates by country. Using daily UN Comtrade statistics, commodity prices and currency exchange rates in U.S. dollars and Japanese yen as available during January 2004 (Malaysia starts in August 2005, and Russia starts in February 2009) through February 2015, they find that: Keep Reading

How do the behaviors of time-series (absolute) and cross-sectional (relative) carry, momentum and value strategies differ? In the November 2015 version of their paper entitled “Dissecting Investment Strategies in the Cross Section and Time Series”, Jamil Baz, Nicolas Granger, Campbell Harvey, Nicolas Le Roux and Sandy Rattray explore time-series and cross-sectional carry, momentum and value strategies as applied to multiple asset classes. They adapt to each asset class the following general definitions:

• Carry – buy (sell) futures on assets for which the forward price is lower (higher) than the spot price.
• Momentum – buy (sell) assets that have outperformed (underperformed) over the past 6-12 months.
• Value – buy (sell) assets for which market price is lower (higher) than estimated fundamental price.

For cross-sectional portfolios, they rank assets within each class-strategy and form portfolios that are long (short) the equally weighted six assets with the highest (lowest) expected returns, rebalanced daily except for currency carry and value trades. For time-series portfolios, they take an equal long (short) position in each asset within a class-strategy according to whether its expected return is positive (negative). When combining strategies within an asset class, they use equal weighting. When combining across asset classes, they scale each class-strategy portfolio to a 15% annualized volatility target. Using daily contract closing bid-ask midpoints for 26 equity futures, 14 interest rate swaps, 31 currency exchange rates and 16 commodity futures during January 1990 through April 2015, they find that: Keep Reading

Are higher even moments of asset return distributions useful predictors of future returns? In the September 2015 version of her paper entitled “A Low-Risk Strategy based on Higher Moments in Currency Markets”, Claudia Zunft explores an adaptive currency trading strategy that exploits the predictive power of higher even moments of forward currency exchange rate returns. The strategy is each month long (short) the equally weighted fifth, or quintile, of currencies with the lowest (highest) higher even return moments relative to recent past levels. For each currency, she first computes 13 even daily return moments over the last month (versus the U.S. dollar) ranging from 4 to 100 and then subtracts from these moments their respective average monthly values over lookback intervals of 12, 24, 36, 48 and 60 months and inception-to-date. From the resulting 78 combinations of moments and lookback intervals, she each month selects the combination with the highest average excess portfolio return over the last three months. For comparison, she also tests long-short quintile carry trade (high interest rate currencies minus low interest rate currencies) and momentum (high prior-month return currencies minus low prior month currencies) portfolios. Using bid, ask and mid-quote spot and forward contract (maturities up to a year) exchange rates versus the U.S. dollar for 20 of the most liquid developed and emerging market currencies as reliably available during December 1989 through October 2014, she finds that: Keep Reading