Statistical Pair Trading on International ETFs

This is sort of a follow up to my post on the research paper on pair trading international ETFs that claimed spectacular results.

For our STAT 434 Financial Time Series final project, my partner and I decided to try and shed some statistical light on the strategy of pair trading international ETFs. We used the above mentioned research paper as our jumping off point.

Basically, the original research paper claimed to find an international ETF pair trading strategy that has a 20% annualized growth rate since 1996 and an almost unbelievably smooth equity curve, essentially printing money no matter the market conditions. Our goal was to statistically test the validity of trading these international ETF pairs, and to develop a more statistically sound international ETF pair trading strategy.

The details are in our paper, but basically we used the Engle-Granger two-step method to select the most cointegrated ETF pairs to trade (betting on pair convergence). Below is our equity curve:

pairtradingequit

Our strategy lost money consistently from 2005 to 2008. This means that instead of betting on the convergence of our ETF pairs, we should have bet on their divergence: there seemed to be momentum in international ETFs during this period, as the prices of even cointegrated pairs diverged even more in the short/medium term. During the financial crisis and Greece/EU panic in the few years during and after 2008, our strategy’s returns improved, which suggests that these international ETF pairs started converging again. This makes intuitive sense: equities do tend to be very correlated with each other in times of economic distress.

We figured we could capture these “regime shifts” with a moving average filter (we tried a 200 day average) on the trading strategy’s equity curve; this entailed “shorting the strategy” (or betting on pair divergence) when the strategy underperformed its moving average, and “going long the strategy” (or betting on pair convergence) when the strategy overperformed its moving average. The resulting strategy had a compound annualized growth rate of about 23% with a Sharpe Ratio close to 1.

filteredequitycurve

The strategy development and backtesting was done in python, with heavy use of the data structures provided by the pandas library. Exploratory data analysis was done in S-plus.

Full paper below:

STAT 434 Rebecca Wu Troy Shu Final Report