Bayesian regime-switching threshold autoregression for nonlinear dynamics and risk forecasting in the Nigerian stock exchange

Financial markets in emerging economies such as Nigeria often exhibit nonlinear dynamics, volatility clustering, and regime shifts that traditional linear models failed to capture. To address these complexities, this study developed a Bayesian Regime-Switching Threshold Autoregressive (RS-TAR) model to analyze daily All-Share Index (ASI) returns in the Nigerian Stock Exchange. The model employed Markov Chain Monte Carlo (MCMC) methods to estimate posterior parameters, transition probabilities, and regime-specific volatility. Results revealed two distinct regimes: a low-volatility state characterized by faster mean reversion and a high-volatility state marked by strong persistence, with the probability of remaining in the crisis regime exceeding ( > 0.8500), indicating prolonged instability once shocks occur. Bayesian posterior predictive distributions were used to compute Value-at-Risk (VaR) and Expected Shortfall (ES), providing robust measures of downside risk. Comparative analysis showed that RS-TAR significantly outperformed benchmark autoregressive (AR) and GARCH models, with Diebold–Mariano tests confirming superior predictive accuracy. The study concludes that RS-TAR is effective in detecting structural breaks, modeling nonlinear market behavior, and enhancing risk forecasting in the Nigerian financial market. These findings underscore the importance of adopting advanced nonlinear and regime-switching approaches in emerging economies, offering practical insights for investors, regulators, and portfolio managers seeking to better understand and manage risks in volatile environments.