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Regime Detection in Latent Factor Models -- A Bayesian Decision-Theoretic Approach

Karl Kristian Engelund · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.20366 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

複数の要因モデルは量的な金融で広く使用されていますが、フィルタリング自体は、エージェントが推定された体制の変化にいつ行動するべきかを決定しません。私たちは、偽の警報、検出遅延および誤った分類をバランスをとるベイエジアの決定問題として、隠れた要因と隠れた体制を含むオンライン体制検出を定義します。後期体制の確率は一般的に非マルコヴィアンであるため、問題は測定された最適な停止問題として表され、モンテカルロのダイナミックプログラミングで粒子フィルタリングを使用して近似されます。単一交換ポイントのヘストンモデルは基準を提供します。観察された変数では、問題は2次元のマルコフフ

Machine translation (Meta M2M100); the English original follows.

Abstract

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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