Statistical arbitrage is a classic quantitative trading strategy that attempts to take advantage of statistical differences in the prices of assets. The strategy is based on the idea that if two assets are not perfectly correlated, then there is an opportunity to profit from the difference in their prices.
Statistical arbitrage is a data-driven approach to trading that relies on statistical analysis to find trading opportunities. It often utilizes processes such as Ornstein–Uhlenbeck to model the asset prices.
Reference [1] proposed a novel trading approach that utilizes a jump-diffusion process to model the overnight price gaps. Its findings are,
In this context, we made four contributions to the literature. The first contribution relates to the developed trading framework based on a jump-diffusion model: we are in a position to capture jumps, mean-reversion, volatility clusters, and drifts. Our approach identifies overnight price gaps based on the jump test of Barndorff-Nielsen and Shephard (2004) and exploits temporary market anomalies by corresponding investments. In a preliminary study, we confirmed the assumption of mean-reverting overnight gaps with the aid of the S&P 500 index. The second contribution focuses on the value-add of our strategy. Therefore, we benchmarked it against well-known quantitative strategies from the same research area, namely the naive S&P 500 buy-and-hold strategy, fixed threshold strategy, general volatility strategy, and reverting volatility strategy. The third contribution is based on our large-scale empirical study on a sophisticated back-testing framework. Our strategy produced statistically- and economically-significant returns of 51.47 percent p.a. after transaction costs; the benchmarks were outperformed. The fourth contribution focuses on the profitable and robust performance results also in the last part of our sample period. Our findings posited a severe challenge to the semi-strong form of market efficiency even in recent times.
In short, using a jump-diffusion model, the authors managed to develop statistical arbitrage trading strategies on SP500 stocks that delivered superior risk-adjusted returns.
In our opinion, this paper addressed an issue that is rarely discussed in the trading literature; that is, the robustness of trading strategies.
Let us know what you think in the comments below.
References
[1] J. Stübinger , L. Schneider, Statistical Arbitrage with Mean-Reverting Overnight Price Gaps on High-Frequency Data of the S&P 500, J. Risk Financial Manag. 2019, 12, 51
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