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 mode and provide statistical properties for two simple estimation methods. The MVF model reflects important properties of volatil- ities, 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. 

Keywords: Volatility modeling, Factor model, High-frequency data, High-dimension, Principal component analysis.