Hidden Markov Models for Detecting Financial Market Regimes

Summary

The document introduces hidden Markov models (HMMs) as a way to infer unobserved market regimes from observed asset returns. It explains the Markov assumption that the next state depends on the current state, and describes how regimes can change return means, volatility, serial dependence, and correlations. Such changes can weaken time-series methods that rely on stable behavior and may inform strategy selection, risk controls, and position sizing.

It outlines the model’s latent states, observations, transition probabilities, and state-dependent observation distributions. It also describes filtering, smoothing, and prediction, with the forward-backward algorithm combining recursive probabilities to estimate hidden states. The examples concern forecasting price movements and identifying market patterns; the document supplies no empirical trading results or comparison against alternatives. It notes that observation distributions need not be Gaussian and gives binary outcomes as a case for a different distribution. HMM estimates remain dependent on modeling assumptions, and the text does not provide implementation details or guidance on validating regime-based strategies.

Key ideas

• An HMM infers hidden market regimes from observed returns or other financial data.
• The Markov property assumes each state transition depends only on the current state.
• Regime changes can alter return distributions and undermine methods that assume stable time-series behavior.
• Filtering estimates the current hidden state, smoothing estimates past states using later observations, and prediction estimates future states.
• The forward-backward procedure combines recursive probabilities to estimate hidden states.
• Observation distributions can be chosen to fit the data rather than assuming Gaussian observations.

Tags

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