A Theoretical Evaluation of the Models for Stock Market Volatility

Abstract

Volatility forecasting has been widely debated in empirical finance, nevertheless, studies examining issues in volatility and their resolution through various models has received a scant attention. Therefore, the present study which is purely a review work aims to elucidate volatility stylised facts along with discussion on theoretical foundation and procedure of volatility forecasting approaches. To serve this purpose, about sixty research papers were reviewed to extract meaningful insights on stock market volatility and its measurement methods. As a whole, it is observed that unconditional models that are intuitive and simple in estimation ignore most of well-known ‘stylised facts’ about volatility. GARCH family models though cater to most of volatility stylised facts, yet at the practioners’ level, EWMA approach appears to be more reliable and worthwhile. Further, studies show that it is difficult to evaluate GARCH models as empirical results of such a model are dependent on the sampling frequency. Hence, choice among such models remains to be an empirical issue sensitive to length and frequency of data. Finally, GARCH family models expected to take care of main stylised facts like, volatility clustering, asymmetric effect, etc., yet models that have a capacity to handle properties like, non-normal behaviour of stock market volatility are beyond the purview of this study, thus represent a future gap for a literature review based research.