Identifying Risk Regimes in a Sectoral Stock Index Through a Multivariate Hidden Markov Framework

Abstract

This study explores the presence of hidden market regimes in a sector-specific stock index within the Thai equity market. The behavior of such indices often deviates from broader macroeconomic trends, making it difficult for conventional models to detect regime changes. To overcome this limitation, the study employs a multivariate Gaussian mixture hidden Markov model, which enables the identification of unobservable states based on daily and intraday return patterns. These patterns include open-to-close, open-to-high, and low-to-open returns. The model is estimated using various specifications, and the best-performing structure is chosen based on the Akaike Information Criterion and the Bayesian Information Criterion. The final model reveals three statistically distinct regimes that correspond to bullish, sideways, and bearish conditions. Statistical tests, particularly the Kruskal–Wallis method, confirm that return distributions, trading volume, and open interest differ significantly across these regimes. Additionally, the analysis incorporates risk measures, including expected shortfall, maximum drawdown, and the coefficient of variation. The results indicate that the bearish regime carries the highest risk, whereas the bullish regime is relatively stable. These findings offer practical insights for regime-aware portfolio management in sectoral equity markets.