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Analytic value function for a pairs trading strategy with a Lévy-driven Ornstein–Uhlenbeck process

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  • Lan Wu
  • Xin Zang
  • Hongxin Zhao

Abstract

This paper studies the performance of pairs trading strategy under a specific spread model. Based on the empirical evidence of mean reversion and jumps in the spread between pairs of stocks, we assume that the spread follows a Lévy-driven Ornstein–Uhlenbeck process with two-sided jumps. To evaluate the performance of a pairs trading strategy, we propose the expected return per unit time as the value function of the strategy. Significantly different from the current related works, we incorporate an excess jump component into the calculation of return and time cost. Further, we obtain the analytic expression of strategy value function, where we solve out the probabilities of crossing thresholds via the Laplace transform of first passage time of the Lévy-driven Ornstein–Uhlenbeck process in one-sided and two-sided exit problems. Through numerical illustrations, we calculate the value function and optimal thresholds for a spread model with symmetric jumps, reveal the non-negligible contribution of incorporating the excess jumps into the value function, and analyze the impact of model parameters on the strategy performance.

Suggested Citation

  • Lan Wu & Xin Zang & Hongxin Zhao, 2020. "Analytic value function for a pairs trading strategy with a Lévy-driven Ornstein–Uhlenbeck process," Quantitative Finance, Taylor & Francis Journals, vol. 20(8), pages 1285-1306, August.
  • Handle: RePEc:taf:quantf:v:20:y:2020:i:8:p:1285-1306
    DOI: 10.1080/14697688.2020.1736613
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    Cited by:

    1. Tim Leung & Kevin W. Lu, 2023. "Monte Carlo Simulation for Trading Under a L\'evy-Driven Mean-Reverting Framework," Papers 2309.05512, arXiv.org, revised Jan 2024.
    2. Kevin W. Lu, 2022. "Calibration for multivariate Lévy-driven Ornstein-Uhlenbeck processes with applications to weak subordination," Statistical Inference for Stochastic Processes, Springer, vol. 25(2), pages 365-396, July.

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