Modelling Stock Market Volatility and Attention

Abstract

This paper aims to model stock market volatility by incorporating attention as an input variable, addressing the inconclusive evidence on its impact on stock volatility. Given the varying importance of attention across studies and the absence of a standard definition, the paper seeks to advance volatility and attention modelling by reviewing and testing different approaches. The study employs a VAR model to examine the relationship between volatility and attention in the U.S. renewable energy sector from 2006 to 2020. Using realized volatility, VIX, trading volume, and Google search volume indices, the research investigates the influence of these variables on the following day's stock price volatility for twenty renewable energy companies. Initial results from the VAR model indicate the statistical significance of some variables, such as prior variance and actual variance, while volume changes appear to have limited significance, contrary to previous findings. The study aligns with existing research, suggesting that attention and trading volume generally increase stock price volatility, whereas attention to industry fundamentals may reduce volatility.