Karl Kristian Engelund · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.20366 · cited by 0 works (OpenAlex) · open-access PDF
Regime-Switching-Multi-Faktor-Modelle werden in der quantitativen Finanzierung weit verbreitet, aber Filterung bestimmt nicht von selbst, wann ein Agent auf abgeleitete Regimeänderungen handeln sollte. Wir formulieren Online-Regime-Erkennung mit latenten Faktoren und versteckten Regimes als Bayesian-Entscheidungsproblem, das falsche Alarme, Detektionsverzögerung und Fehlklassifizierung ausgleicht. Da die Wahrscheinlichkeiten des späteren Regimes im Allgemeinen nicht-Markovian sind, wird das Problem als ein messbar bewertet optimales Stop-Problem dargestellt und durch Partikelfilterung mit Monte Carlo-dynamischer Programmierung annähernd veranschlagt. Ein Einzelwechselpunkt-Heston-Modell bietet einen Benchmark. Mit
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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