Karl Kristian Engelund · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.20366 · cited by 0 works (OpenAlex) · open-access PDF
Regime-switching multi-factor models are widely used in quantitative finance, but filtering does not by itself determine when an agent should act on inferred regime changes. We formulate online regime detection with latent factors and hidden regimes as a Bayesian decision problem balancing false alarms, detection delay, and misclassification. Since posterior regime probability are generally non-Markovian, the problem is represented as a measured optimum stopping problem and approximated using particle filtration with Monte Carlo dynamic programming. A single-change-point Heston model provides a benchmark. With observed variance, the problem reduces to a two-dimensional Markov free-boundary problem, while under variance latency decisions are based on the posterior filtered measure. Heston and regime-switching Vasek
Machine translation (Meta M2M100); the English original follows.
Regime-switching multi-factor models are widely used in quantitative finance, but filtering does not by itself determine when an agent should act on inferred regime changes. We formulate online regime detection with latent factors and hidden regimes as a Bayesian decision problem balancing false alarms, detection delay, and misclassification. Since posterior regime probabilities are generally non-Markovian, the problem is represented as a measure-valued optimal stopping problem and approximated using particle filtering with Monte Carlo dynamic programming. A single-change-point Heston model provides a benchmark. With observed variance, the problem reduces to a two-dimensional Markov free-boundary problem, while under latent variance decisions are based on the filtered posterior measure. Numerical experiments in Heston and regime-switching Vasicek models show that the latent-information procedure can outperform feasible plug-in rules.
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Topics: Particle filter, Bayesian probability, Markov chain Monte Carlo, Monte Carlo method, Variance (accounting), Computer science
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