On a threshold heteroscedastic model

This paper proposes a threshold heteroscedastic model which integrates threshold nonlinearity and GARCH-type conditional variance for modeling mean and volatility asymmetries in financial markets. The main feature of this model is that the threshold variable for regime switching is formulated as a weighted average of important auxiliary variables. Estimation and diagnostic checks are performed using Markov chain Monte Carlo methods. Forecasts of volatility and value at risk can also be generated from predictive distributions. The proposed methodology is illustrated using both simulated and actual international market index data. Empirical results show higher average volatility and more persistent volatility when bad news arrives. While the domestic return is the major determinant of the regimes, both the SP 500 and Nikkei 225 indices also impact the dynamic structure of domestic market returns.