Generalized dynamic factor models and volatilities: recovering the market volatility shocks

Decomposing volatilities into a common market-driven component and an idiosyncratic item-specific component is an important issue in financial econometrics. However, this requires the statistical analysis of large panels of time series, and hence faces the usual challenges associated with high-dimensional data. Factor model methods in such a context are an ideal tool, but they do not readily apply to the analysis of volatilities. Focusing on the reconstruction of the unobserved market shocks and the way they are loaded by the various items (stocks) in the panel, we propose an entirely non-parametric and model-free two-step general dynamic factor approach to the problem, which avoids the usual curse of dimensionality. Applied to the Standard & Poor's 100 asset return data set, the method provides evidence that a non-negligible proportion of the market-driven volatility of returns originates in the volatilities of the idiosyncratic components of returns.