Factor Modeling for Volatility; Yingying Li ( Hong Kong University of Science and Technology)

Abstract

Under a high-frequency and high-dimensional setup, we establish a framework to estimate the factor structure in stock volatility. We show the consistency of conducting principal component analysis on realized volatilities in identifying the factor structure in stock and idiosyncratic volatility. Empirically, with strong empirical evidence, we propose a single factor model for stock volatility, where volatility is represented by a common volatility factor and a multiplicative lognormal idiosyncratic component. We further utilize the proposed factor model for volatility forecasting and show that our proposed approach outperforms various benchmark methods. This is joint work with Yi Ding, Robert Engle and Xinghua Zheng.