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Can Internet Search Queries Help to Predict Stock Market Volatility?

机译:互联网搜索查询可以帮助预测股市波动吗?

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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.
机译:我们研究了股市波动的动态以及散户对股市的关注。后者是通过与领先股票市场指数相关的互联网搜索查询来衡量的。我们发现道琼斯实现的波动性及其名称的搜索查询量之间存在强烈的共鸣。此外,搜索查询会产生Granger引起的波动性:今天搜索量增加,明天波动性增加。在已实现的波动率的自回归模型中包含搜索查询可以改善针对不同预测范围(尤其是在高波动率阶段)的样本内,样本外波动率预测。

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