Currency Trading

Does the currency carry trade, financing short-term deposits in currencies with high interest rates with short-term loans in currencies with low interest rates (or being long and short forward contracts in currencies with high and low interest rates) generate a reliably attractive return? In the November 2014 version of their paper entitled “Empirical Evidence on the Currency Carry Trade, 1900-2012”, Nikolay Doskov and Laurens Swinkels measure annual nominal and real carry trade returns for a large sample of currencies over a long period covering multiple currency regimes. They use yields on local Treasury bills (T-bills) or equivalents to approximate short-term interest rates and make some adjustments to account for government defaults. To estimate carry trade returns, they sort currencies each year based on associated T-bill yields and take equally weighted long (short) positions in the four currencies with the highest (lowest) yields. Using annual exchange rates and associated T-bill yields for 20 currencies during 1900 through 2012 (19 currencies before 1925 and 12 currencies after 1998), they find that: Keep Reading

Does quantitative technical analysis work reliably in currency trading? If so, where does it work best? In their May 2013 paper entitled “Forty Years, Thirty Currencies and 21,000 Trading Rules: A Large-Scale, Data-Snooping Robust Analysis of Technical Trading in the Foreign Exchange Market”, Po-Hsuan Hsu and Mark Taylor 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 also test whether technical trading effectiveness weakens over time. In testing robustness to trading frictions, they assume a fixed one-way trading cost of 0.025%. Using daily U.S. dollar exchange rates for nine developed market currencies and 21 emerging market currencies during January 1971 through July 2011, they find that:

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In his August 2014 paper entitled “Bitcoin Myths and Facts”, Campbell Harvey examines eight claims about bitcoin. One of these claims is that bitcoin is currently too volatile to serve as a store of value. Using daily data for the dollar-bitcoin exchange rate during mid-July 2010 through mid-August 2014, he finds that: Keep Reading

Are simple moving averages (SMA) effective in generating signals for short-term currency trading? In the April 2014 draft of his paper entitled “ANANTA: A Systematic Quantitative FX Trading Strategy”, Nicolas Georges investigates the effectiveness of fast (2-day) and slow (15-day) SMAs as indicators of currency exchange rate evolutions when applied to ten G10 currency pairs and aggregated. His objective is to buy (sell) currencies expected to appreciate (depreciate) based on aggregation of binary signals (see the first chart below). He rebalances the portfolio twice daily when liquidity is high at the London and New York closes. He uses market orders and includes actual trading costs unique to each currency pair, based on bid-ask spreads ranging from 0.0036% to 0.035%. He does not use stop-losses. He compiles results in U.S. dollars. Using twice daily exchange rates for G10 currency pairs during January 2003 through December 2013, he finds that: Keep Reading

What is the best way to generate price trend signals for trading futures/forward contracts? In their December 2013 paper entitled “CTAs – Which Trend is Your Friend?”, Fabian Dori, Manuel Krieger, Urs Schubiger and Daniel Torgler compare risk-adjusted performances of three ways of translating trends into trading signals:

1. Binary signals (up or down) trigger 100% long or 100% short trades. When trends are strong (ambiguous), this approach generates little trading (whipsaws/over-commitment to weak trends). The price impact of trading via this approach may be substantial for large traders.
2. Continuously scaled signals trigger long or short trades with position size scaled according to the strength of up or down trend; the stronger the trend, the larger the position. Changes in trend strength generate incremental position adjustments.
3. Empirical distribution signals trigger long or short trades with position size scaled according to the historical relationship between trend strength and future return. The strongest trend may not indicate the strongest future return, and may actually indicate return (and therefore position) reversal. Changes in trend strength generate position adjustments.

They test these three approaches for comparable trends exhibited by 96 futures/forward contract series, including: 30 currency pairs, 19 equity indexes, 11 government bond indexes, 8 short-term interest rates (STIR) and 28 commodities. They consider two risk-adjusted return metrics: annualized return divided by annualized volatility, and annualized return divided by maximum drawdown. They ignore trading frictions. Using prices for these 96 series from 1993 to 2013, they find that: Keep Reading

Are currency traders the last ones to know? In the February 2014 draft of their paper entitled “Cross-Asset Return Predictability: Carry Trades, Stocks and Commodities”, Helen Lu and Ben Jacobsen investigate whether commodity and stock index returns predict currency carry trade performance. They consider equally weighted carry trade strategies that each month buy (sell) one-month forward contracts for the one, two or three currencies with the highest (lowest) beginning-of-month interest rates and hold to maturity. They account for bid-ask spreads and express profits in U.S. dollars. They evaluate the power of three commodity indexes (CRB Spot, CRB Raw Industrials Spot and CRB Metals Spot) and three total return equity indexes (MSCI All Country, MSCI World and S&P 500) to predict carry trade profitability. Using monthly levels of the commodity and stock indexes and monthly one-month forward rates and spot rates for the G-10 currencies during February 1988 through December 2011, they find that: Keep Reading

Do country economic fundamentals provide exploitable information about future changes in associated currency exchange rates? In the June 2013 version of their paper entitled “Currency Risk Premia and Macro Fundamentals”, Lukas Menkhoff, Lucio Sarno, Maik Schmeling and Andreas Schrimpf investigate the usefulness of economic fundamentals in currency trading by measuring the performance of multi-currency hedge portfolios formed by sorting on lagged economic variables across 35 countries. They take the perspective of a U.S. investor by measuring all exchange rates versus the U.S. dollar. The country economic variables they consider are: (1) interest rates; real Gross Domestic Product (GDP) growth; real money growth (from currency in circulation); and, real exchange rates. They calculate growth rates based on 20-quarter rolling averages. They form hedge portfolios from extreme fourths (quartiles) of ranked currencies, rebalanced annually at year end, and calculate returns in excess of short-term interest rates. Using quarterly currency exchange rate, short-term interest rate, real GDP, Consumer Price Index (CPI) and currency in circulation for 35 countries/currencies for out-of-sample testing from the first quarter of 1974 through the third quarter of 2010, they find that: Keep Reading

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