Generating Composite Volatility Forecasts with Radom Factor Betas

Publisher Summary Out-of-sample volatility forecasts are of crucial importance in virtually all financial decisions such as portfolio selection, the pricing of primary and derivative assets, and risk management methodologies. This chapter presents a methodology for generating composite out-of-sample volatility forecasts when returns are driven by random beta multiple factor processes. Given time varying factor and idiosyncratic conditional variances, analysis focuses on two cases: conditionally autoregressive betas in which the factor and its beta share the same innovation and latent autoregressive betas in which the factor and its beta experience distinct but correlated innovations. The literature has proposed both direct and indirect parameterization methodologies to model the temporal characteristics of beta coefficients, with different implications for volatility forecasting. In direct parameterization, beta coefficients are modeled as random parameters in that beta consists of a constant plus a noise term. Indirect approaches to modeling consider the beta coefficient as the ratio of the asset and factor conditional covariance over the factor conditional variance and model the numerator and the denominator of the ratio separately.