Multiplicative factor model for volatility

Abstract

Facilitated with high-frequency observations, we introduce a remarkably parsimonious one-factor volatility model that offers a novel perspective for comprehending daily volatilities of a large number of stocks. Specifically, we propose a multiplicative volatility factor (MVF) model, where stock daily variance is represented by a common variance factor and a multiplicative idiosyncratic component. We demonstrate compelling empirical evidence supporting our model and provide statistical properties for two simple estimation methods. The MVF model reflects important properties of volatilities, applies to both individual stocks and portfolios, can be easily estimated, and leads to exceptional predictive performance in both US stocks and global equity indices.