Average Stock Variance and Market Returns:
Evidence of Time-Varying Predictability at the
Daily Frequency

Abstract

Chen, Ortiz-Molina, and Zhang develop a daily measure of average stock variance and study whether it can predict market returns one day ahead. Using a time-invariant prediction model, they find a robust predictive relation between these variables that cannot be used to profitably time the market. A closer look reveals that the strength and even the direction of the predictive relation vary significantly over short periods of time. Moreover, a simple timing strategy that exploits this variation over time significantly outperforms the market buy-and-hold strategy in terms of the mean-variance trade-off. The evidence shows that predictability is stronger during business cycle contractions and that the timing strategy is profitable because it avoids losses during bad times. The evidence also shows that parameter breaks occur very frequently over short periods of time, and not only when the economy switches from one phase of the business cycle to another. The authors’ results suggest that idiosyncratic risk matters in asset pricing and that its effect is time varying.