A HOLISTIC APPROACH TO THE PREDICTIVE POWER OF EXPECTED VOLATILITY
综合多种波动率测度和实证方法，发现总波动率和特质波动率的水平及变化对股票超额收益有预测能力，其中EWMA、GARCH和TGARCH测度预测力最强。
Abstract The existing literature is highly dispersed regarding the relation between volatility and expected returns. We combine several volatility measures and empirical methods to give a holistic overview of this fundamental relation in finance. Results indicate that total and idiosyncratic volatility levels and volatility changes have predictive power in the cross‐section of expected excess stock returns. Volatility levels are positively and volatility changes are negatively related to future stock returns. Exponentially weighted moving average (EWMA), generalized autoregressive conditional heteroskedasticity (GARCH), and threshold GARCH (TGARCH) volatility measures have the greatest predictive power. Controlling for the short‐term reversal effect and illiquidity does not help explain the predictive power of expected volatility.