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A Comparative Modelling of Essential Characteristics of Volatility |
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PP: 241-270 |
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doi:10.18576/jsap/140208
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Author(s) |
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Richard T. A. Samuel,
Charles Chimedza,
Caston Sigauke,
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Abstract |
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| This study utilised the dynamics of three 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. The study further used a parametric model through the ARFIMA-FIGARCH models, and three semi-parametric approaches via the log periodogram estimator of Geweke and Porter-Hudak (GPH), the local Whittle estimator, and the exact local Whittle estimator to estimate and determine the presence of long memory in the returns and the return volatility, i.e., squared returns and absolute values of returns. The results of the estimations indicate that the daily returns, squared returns, and absolute returns exhibit long memory, hence, shocks decay at a slower rate. However, the persistence is lower in the returns when compared with the squared returns and absolute returns. Our findings from the long-memory decomposition 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.
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