Stock market volatility has been an important subject in the finance literature for which now an enormous body of research exists. Volatility modelling and forecasting have been in the epicentre of this line of research and although more than a few models have been proposed and key parameters on improving volatility forecasts have been considered, finance research has still to reach a consensus on this topic. This thesis enters the ongoing debate by carrying out empirical investigations by comparing models from the current pool of models as well as exploring and proposing the use of further key parameters in improving the accuracy of volatility modelling and forecasting. The importance of accurately forecasting volatility is paramount for the functioning of the economy and everyone involved in finance activities. For governments, the banking system, institutional and individual investors, researchers and academics, knowledge, understanding and the ability to forecast and proxy volatility accurately is a determining factor for making sound economic decisions. Four are the main contributions of this thesis. First, the findings of a volatility forecasting model comparison reveal that the GARCH genre of models are superior compared to the more ‘simple' models and models preferred by practitioners. Second, with the use of backward recursion forecasts we identify the appropriate in-sample length for producing accurate volatility forecasts, a parameter considered for the first time in the finance literature. Third, further model comparisons are conducted within a Value-at-Risk setting between the RiskMetrics model preferred by practitioners, and the more complex GARCH type models, arriving to the conclusion that GARCH type models are dominant. Finally, two further parameters, the Volatility Index (VIX) and Trading Volume, are considered and their contribution is assessed in the modelling and forecasting process of a selection of GARCH type models. We discover that although accuracy is improved upon, GARCH type forecasts are still superior.

This study investigates how term structure dynamics of VIX futures can be exploited forabnormal returns. To be able to access volatility as a tradeable asset, the trading strategiesonly trades ETFs which are designed to replicate the movements of VIX futures index. Itis established that such ETFs are unsuitable for buy-and-hold investments because of thenegative roll yield it usually suffers, caused by the slope of the VIX term structure.Consequently, these conditions create opportunities for strategies that use direct andinverse VIX ETFs to be profitable. The study is a quantitative study that uses historicalprice data to back test three different trading strategies. The strategies are tested over theperiod 11-oct-2011 to 31-mar-2020. The authors have deliberately chosen to delimit thestudy by not testing the performance of the ETFs, not statistically test the risk-adjustedreturns and not perform a regression to calculate optimal hedge ratios for the strategies.The results from this study shows that its possible for strategies that exploit the termstructure dynamics of VIX futures to generate abnormal returns.