A flexible regime switching model with pairs trading application to the S&P 500 high-frequency stock returns

Abstract

This paper develops the regime classification algorithm and applies it within a fully-fledged pairs trading framework on minute-by-minute data of the S&P 500 constituents from 1998 to 2015. Specifically, the highly flexible algorithm automatically determines the number of regimes for any stochastic process and provides a complete set of parameter estimates. We demonstrate its performance in a simulation study—the algorithm achieves promising results for the general class of Lévy-driven Ornstein–Uhlenbeck processes with regime switches. In our empirical back-testing study, we apply our regime classification algorithm to propose a high-frequency pair selection and trading strategy. The results show statistically and economically significant returns with an annualized Sharpe ratio of 3.92 after transaction costs—results remain stable even in recent years. We compare our strategy with existing quantitative trading frameworks and find its results to be superior in terms of risk and return characteristics. The algorithm takes full advantage of its flexibility and identifies various regime patterns over time that are well-documented in the literature.

Keywords:

• Finance
• Pairs trading
• Statistical arbitrage
• Markov regime switching
• Lévy-driven Ornstein–Uhlenbeck process
• High-frequency data

JEL Classification:

• C1
• C3
• C5
• C6
• C63

Acknowledgments

We are grateful to Ingo Klein, Julian Knoll, and two anonymous referees for many helpful discussions and suggestions on this topic.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

† In the following, the terms regime and state are used synonymously.

† In Mai (2014) the mean-reversion rate is denoted as drift parameter.

† This setting is well in line with the length of our simulated time series in section 3.2.