Nonlinear dependence modeling with bivariate copulas: Statistical arbitrage pairs trading on the S&P 100

Abstract

We develop a copula-based pairs trading framework and apply it to the S&P 100 index constituents from 1990 to 2014. We propose an integrated approach, using copulas for pairs selection and trading. Essentially, we fit t-copulas to all possible combinations of pairs in a 12 month formation period. Next, we run a 48 month in-sample pseudo-trading to assess the profitability of mispricing signals derived from the conditional marginal distribution functions of the t-copula. Finally, the most suitable pairs based on the pseudo-trading are determined, relying on profitability criteria and dependence measures. The top pairs are transferred to a 12 month trading period, and traded with individualized exit thresholds. In particular, we differentiate between pairs exhibiting mean-reversion and momentum effects and apply idiosyncratic take-profit and stop-loss rules. For the top 5 mean-reversion pairs, we find out-of-sample returns of 7.98 percent per year; the top 5 momentum pairs yield 7.22 percent per year. Return standard deviations are low, leading to annualized Sharpe ratios of 1.52 (top 5 mean-reversion) and 1.33 (top 5 momentum), respectively. Since we implement this strategy on a highly liquid stock universe, our findings pose a severe challenge to the semi-strong form of market efficiency and demonstrate a sophisticated yet profitable alternative to classical pairs trading