Fatih Yilmaz, formerly of Bank of America (currently BlueGold), has a piece called "Imaginal Spreads and Pairs Trading" on exactly this topic, if you can find it (I couldn't find a copy on the public internet), originally published April 17, 2009. He writes:
Academics and industry practitioners generally concentrate on time series aspects of currency markets.
This is primarily the result of limited number of traded currencies. Most markets for emerging currencies
are far from ignorable frictions. Hence, if one is confined to G10 currency markets, considering that
shocks to the USD typically account more than 50% of variations in G10 (see Figure 1), not much room is
left for cross-sectional selection skills or market neutral strategies. Pairs trading is a market-neutral (or
USD-neutral) strategy and it can capture different opportunities within G10 currency markets from a
statistical point of view. Moreover, given the strategy sells winners and buys losers, it is likely to be low or
negatively correlated with most traditional directional models (such as momentum strategy).
Our focus in this note is to test pairs trading strategy within G10 currency markets. Our currency data set is
monthly (end of month data obtained from Reuters and DataStream) and we use short-term money market
rates for carry calculations (obtained from DataStream). Our data set is from 1973-2009. We take the USD
as the numéraire currency and form 36 possible pairs using all 9 USD crosses. Our pairs-matching
algorithm and trading strategy is described below:
Excess returns, Sharpe ratios and directional accuracy
statistics generally indicate promising results. In particular, as we increase threshold misalignment
level for trading signals, all performance statistics tend to improve. Generally speaking, misalignment
levels around 1.5-2.0 standard deviations tend to produce consistently good results.
The Sharpe Ratios he refers to are about 0.7-0.8 for a 3M holding period.
The presented results in this note should be taken as exploratory. Nevertheless, the first set of results
appears to be encouraging for several reasons. Performance statistics are relatively attractive and robust for
an active G10 strategy. Especially considering that the strategy is USD neutral and the forecast horizon is
over 25y. Moreover, the strategy is contrarian and concentrates on relative value trades. Hence, likely to
produce low correlated returns to traditional directional currency models. If we take the presented results
in this note at face value, then we should ask an important question: what are we being paid for?
Transaction costs in this study are not relevant given that we used monthly data. Bankruptcy and liquidity
risks and short sales constraints can generally be ignored for G10 currency markets. It would be interesting
to analyse the correlation of pairs strategy returns with macroeconomic and related asset market cycles (i.e.
time-varying risk premia for cycles). In their study, Goetzmann et. al. (2006) argue that the pairs strategy
might be rewarding because of (hidden or latent) common factor as a main driver of the equities they
analyse. There is always the risk of being arbitraged away; however the strategy appears to produce
relatively robust results even in the past decade or so (when hedge fund activity increased significantly).
Other possibilities might be that the strategy can be rewarding for pushing markets towards equilibrium via
arbitrage trades. Given that the strategy is market neutral and relies on relative value trades, there is also
the risk of missing strong market moves with such a strategy from a model allocation point of view. In any
case, in our view, understanding fundamental risk-reward characteristics of such a strategy is important
and requires further analysis in this context.