Cross-sectional return dispersion and volatility prediction - Fei, Tianlun; Liu, Xiaoquan; Wen, Conghua (2019)
We use intraday and daily data to examine the impact of cross-sectional return dispersion on volatility forecasting in the Chinese equity market. We adopt the GARCH, GJR-GARCH, and HAR models and, by augmenting them with return dispersion measures, provide empirical evidence that the return dispersion exhibits substantial information in describing the volatility dynamics by generating signicantly lower forecasting errors at market and industry levels. Furthermore, the information content of the return dispersion tends to o er economic gain to a mean-variance utility investor. The ndings are robust with respect to alternative volatility proxies, subsample analysis, and alternative market-wide stock indices.