Detecting Regime Changes in Financial Markets using Hidden Markov Models and Directional Changes

Abstract

The purpose of this study is to construct a multivariate input based Hidden Markov model based on directional changes to detect regime changes in financial markets. For this study, a Hidden Markov Model with multivariate inputs was used. Directional changes were used on historical S&P 500 index returns, additionally, Chicago Board Options Exchange's CBOE Volatility Index along with commonly used index performance indicators were used as inputs to the Hidden Markov Model. The Hidden Markov Model with Directional Change indicators is able to effectively classify regimes on the basis of their statistical properties viz. Mean and standard deviation. The motivation of this study is to model and detect regime changes in US financial markets over the 22-year period from 2000 to 2022. Hidden Markov Models have historically been used by modelling index returns using time series analysis and realised volatility, this paper uses directional changes along with other inputs as observed states to the Hidden Markov Model like the Chicago Board Options Exchange's CBOE Volatility index, 22 and 66 day returns of the S&P 500 index in addition to the 22-day volatility of the S&P 500 index.