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

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