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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

Chinese

政权交换的多因素模型在定量金融中广泛使用,但过滤本身并不决定何时一个代理应对推断的政权变化采取行动。我们将在线政权检测与隐藏的因素和隐藏的政权制定为比亚西亚决策问题,平衡虚假警报,检测延迟和错误分类。由于后期政权的概率一般不是马尔科夫,这个问题被描述为一个测量值的最佳停止问题,并通过使用粒子过滤与蒙特卡洛动态编程来接近。单个交换点的赫斯顿模型提供了基准。随着观察到的差异性,这个问题减少到一个两维的马尔科夫自由边界问题,而在变量延迟下,决策是基于后面的过滤措施。在赫斯顿和

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

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange