This study utilised the dynamics of five time-varying models to estimate six essential features of financial return volatility that are relevant for robust risk management. These features include pronounced persistence, mean reversion, leverage effect or volatility asymmetry, conditional skewness, conditional fat-tailedness, and the long memory behaviour of volatility decomposition into long-term and short-term components. Both simulation and empirical evidence are provided. Through the applications of these models using the S&P Indian index, the study shows that the market returns are characterised by these volatility features. Our findings from the long-memory behaviour revealed that although the response to shocks is greater in the short-term component, it is however short-lived. On the contrary, despite a high degree of persistence in the long-term component, market information or unexpected news arrival only has a low long-run impact on the market. Based on this, the long-run investment risks within the Indian stock market seem to be under control. Hence, our findings suggest that rational investors should try to stay calm with the arrival of unexpected news in the market because the long-run effect of such news will not be severe, and the market will eventually return to its normal state.
Samuel et al. (Mon,) studied this question.