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

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  • Sylvia Endres
  • Johannes Stübinger

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.

Suggested Citation

  • Sylvia Endres & Johannes Stübinger, 2019. "A flexible regime switching model with pairs trading application to the S&P 500 high-frequency stock returns," Quantitative Finance, Taylor & Francis Journals, vol. 19(10), pages 1727-1740, October.
  • Handle: RePEc:taf:quantf:v:19:y:2019:i:10:p:1727-1740
    DOI: 10.1080/14697688.2019.1585562
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    Cited by:

    1. Carè, Rosella & Cumming, Douglas, 2024. "Technology and automation in financial trading: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 71(C).
    2. Viviana Fanelli & Claudio Fontana & Francesco Rotondi, 2023. "A hidden Markov model for statistical arbitrage in international crude oil futures markets," Papers 2309.00875, arXiv.org, revised Sep 2024.
    3. Tian-Shyr Dai & Yi-Jen Luo & Hao-Han Chang & Chu-Lan Kao & Kuan-Lun Wang & Liang-Chih Liu, 2024. "Asymptotic analyses for trend-stationary pairs trading strategy in high-frequency trading," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1391-1411, November.

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