Understanding and Forecasting Stock Market Volatility Through Wavelet Decomposition, Statistical Learning and Econometric Methods

Abstract

Volatility in stock markets evokes varying responses from market participants. While some perceive it as opportunity to make money, others perceive it as a threat and start unwinding their positions. In today’s globalized environment, increased volatility reflects not only the domestic macroeconomic state, but also global uncertainty. While volatility in the stock market as a whole can be influenced by events like oil price shocks, increase in rates of interest in the US and domestic elections, volatility in individual stock prices can be due to perceived growth prospects of the company or the sector, company specific news or policy announcements that can affect a company/sector. In this study, associations, causal influence among three volatility indicators namely, CBOE VIX, INDIA VIX and Historic Volatility (HV) have been carefully examined, and predictive models for forecasting have been developed. An integrated framework incorporating Wavelet decomposition, statistical predictive modeling and standard econometric methods is presented to accomplish the research objectives.