Can Internet Search Queries Help to Predict Stock Market Volatility?
研究了互联网搜索查询量（反映零售投资者对股市的关注度）与道琼斯指数已实现波动率的关系，发现搜索量能格兰杰引起波动率变化，且加入搜索量可改进波动率预测，尤其在高波动时期。
Abstract We study the dynamics of stock market volatility and retail investors' attention to the stock market. The latter is measured by internet search queries related to the leading stock market index. We find a strong co‐movement of the Dow Jones' realised volatility and the volume of search queries for its name. Furthermore, search queries Granger‐cause volatility: a heightened number of searches today is followed by an increase in volatility tomorrow. Including search queries in autoregressive models of realised volatility improves volatility forecasts in‐sample, out‐of‐sample, for different forecasting horizons, and in particular in high‐volatility phases.