Generalized Dynamic Factor Models and Volatilities Estimation and Forecasting

In large panels of financial time series with dynamic factor structure on the levels or returns, the volatilities of the common and idiosyncratic components often exhibit strong correlations, indicating that both are exposed to the same market volatility shocks. This suggests, alongside the dynamic factor decomposition of returns, a dynamic factor decomposition of volatilities or volatility proxies. Based on this observation, Barigozzi and Hallin (2016) proposed an entirely non-parametric and model-free two-step general dynamic factor approach which accounts for a joint factor structure of returns and volatilities, and allows for extracting the market volatility shocks. Here, we go one step further, and show how the same two-step approach naturally produces volatility forecasts for the various stocks under study. In an applied exercise, we consider the panel of asset returns of the constituents of the S&P100 index over the period 2000–2009. Numerical results show that the predictors based on our two-step method outperform existing univariate and multivariate GARCH methods, as well as static factor GARCH models, in the prediction of daily high–low range—while avoiding the usual problems associated with the curse of dimensionality