Abstract
AbstractCombining 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.
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