Abstract

Combining the four aspects of self-, macro, environmental, and policy attention, using backward-looking rolling regressions, we construct novel international and domestic investor-attention indices using the search volume index from Google Trends together with Baidu Index to investigate how investor attention affects the CNY-CNH spreads volatility. Moreover, comparing different GARCH-MIDAS models and conventional GARCH-type models is conducted concerning the out-of-sample volatility forecasting capability. Our results show that: (i) international and domestic investor attention has a positive impact; and (ii) the GARCH-MIDAS models involving investor attention improve forecast accuracy. In particular, the model with domestic investor attention has an advantage in forecasting.