Predictable Components of Individual Stock Volatility

Abstract

We estimate the conditional variance of daily stock returns using an extended GARCH model with event-related dummy variables to capture the predictable components of volatility change, such as earnings announcements, macroeconomic announcements, day-of-the-week effects, etc. We examine the out-of-sample forecasting ability and find this model provides a better performance compared to the usual GARCH(1,1) volatility model. In addition, we find that the dependence on the random components increases after we include the predictable components. This implies that modeling volatilities using only past returns without other predictable variables could underestimate the persistence levels of volatilities and thus bias the volatility forecasts, especially those over long horizons.