Hidden Markov Models for Financial Market Regime Detection

Summary

The article introduces Hidden Markov Models (HMMs) as a way to represent market regimes that cannot be observed directly but affect visible asset returns. Regimes may correspond to changing return behavior, volatility, serial dependence, or correlations. In an HMM, latent states move according to transition probabilities, while observations are modeled conditionally on the current state. The article places HMMs among Markov models and distinguishes them from fully observed chains and controlled decision processes.

For trading, inferred regime probabilities could help determine when to deploy or adjust strategies, risk controls, and position sizes. The discussion distinguishes filtering, which estimates the current state using observations available so far, from smoothing, which revises estimates of past states using later information. It outlines the application conceptually, while leaving algorithm derivations and empirical evaluation to other work. No results are presented to show that regime overlays improve performance, and the model depends on assumptions about state transitions and observation distributions.

Key ideas

• An HMM treats market regimes as latent states and asset returns as observations influenced by those states.
• State transitions depend on the current state under the Markov assumption.
• Filtering estimates the current regime from observations available up to the present.
• Regime estimates could inform strategy deployment, risk management, and position sizing.
• The article outlines applications but does not provide performance evidence for regime-based trading.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.