Particle Filtering Estimation of Regime Switching Factor Model and Its Application in Statistical Arbitrage Strategy

Yu Mu () and Robert J. Frey ()
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Yu Mu: Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
Robert J. Frey: Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794, USA

JRFM, 2025, vol. 18, issue 10, 1-22

Abstract: Statistical factor models are widely applied across various domains of the financial industry, including risk management, portfolio selection, and statistical arbitrage strategies. However, conventional factor models often rely on unrealistic assumptions and fail to account for the fact that financial markets operate under multiple regimes. In this paper, we propose a regime-switching factor model estimated via a particle filtering algorithm, which is a Monte Carlo-based method well-suited for handling nonlinear and non-Gaussian systems. Our empirical results show that incorporating dynamic structure and a regime-switching mechanism significantly enhances the model’s ability to detect structure breaks and adapt to evolving market conditions. This leads to improved performance and reduced drawdowns in the equity statistical arbitrage strategies.

Keywords: regime switching; dynamic factor model; particle filtering; statistical arbitrage strategies (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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