Does Google search index really help predicting stock market volatility? Evidence from a modified mixed data sampling model on volatility

作者：

Highlights：

• We develop a novel multiple factors GARCH-MIDAS (MF-GARCH-MIDAS) model.

• It allows for a long-run volatility driven by multiple factors sampled at different frequencies.

• It is used to investigate the usefulness of Google trends in predicting stock market volatility.

• The empirical results show favorable evidence about our proposed model.

• We find that Google trends really matters for volatility forecasting.

摘要

•We develop a novel multiple factors GARCH-MIDAS (MF-GARCH-MIDAS) model.•It allows for a long-run volatility driven by multiple factors sampled at different frequencies.•It is used to investigate the usefulness of Google trends in predicting stock market volatility.•The empirical results show favorable evidence about our proposed model.•We find that Google trends really matters for volatility forecasting.

论文关键词：Volatility forecasting,Google trends,Mixed frequency data,GARCH-MIDAS,Multiple factors GARCH-MIDAS

论文评审过程：Received 26 June 2018, Revised 18 December 2018, Accepted 20 December 2018, Available online 26 December 2018, Version of Record 23 January 2019.

论文官网地址：https://doi.org/10.1016/j.knosys.2018.12.025