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Given the rapid growth in financial markets over the past 20 years, along with the explosive development of new and more complex financial instruments, an ever-growing need has emerged for accurate and efficient volatility forecasting to use in numerous practical applications of financial data such as the analysis of market timing decisions, assistance in portfolio selection, and estimates of variance in option pricing models. Furthermore, accurate volatility estimates are also vital in areas such as risk management for the calculation of metrics in hedging and Value-at-Risk (VaR) policies.
Since the 1987 stock market crash, modeling and forecasting financial market volatility has received a great deal of attention from academics, practitioners and regulators due to its central role in several financial applications, including option pricing, asset allocation and hedging (Busch et al., 2011). In addition, the financial world has witnessed bankruptcy or near bankruptcy of various institutions that incurred huge losses due to their exposure to unexpected market moves for more than a decade (Liu et al., 2009). These financial disasters have further highlighted the significance of volatility forecasting in risk management (calculating VaR). Given these facts, the quest for accurate forecasts appears to be still going on in the recent years.
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