In this paper, we study how different categories of crucial COVID-19 information influence price dynamics in stock and option markets during the period from 01/21/20 to 01/31/21. We present a theoretical model in which the behavioral traders make perceptual errors based on the intensity of sentiment arising from different types of news. In addition to the magnitude and direction of the news and its payoff relevance to security prices, other factors such as fear, emotion, and social media can influence the sentiment level. Using Google search data, we construct novel proxies for the sentiment levels induced by five categories of news, COVID, Market, Lockdown, Banking, and Government relief efforts. If the relative presence of behavioral traders in the stock market exceeds that in the option market, different predictions obtain for the effect of sentiment indices on jump volatility of the VIX index, the S&P 500 index, and the S&P 500 Banks index. We find that the jump component in the VIX index is increasing significantly with COVID index, Market index, Lockdown index, and Banking index. However, only COVID index and Market index increase the jump component of realized volatility of the stock indices (S&P 500 index and S&P 500 Banks index). The Government relief efforts index decreases this jump component. Banking and Lockdown index reduce jump volatility in the S&P 500 index and S&P 500 Banks index, but only with a delay of 5 days. These results are consistent with the predictions of our model.
Keywords: Banking; COVID-19, Coronavirus; Google search index; Government relief; Jumps; Lockdown; Market; News; Sentiment index; Textual analysis; VIX; Virus; Volatility.
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