Forecasting equity index volatility by measuring the linkage among component stocks
linkage among the realized volatilities of component stocks is important when
modeling and forecasting the relevant index volatility. In this article, the
linkage is measured via an extended Common Correlated Effects (CCEs) approach
under a panel heterogeneous autoregression model where unobserved common
factors in errors are assumed. Consistency of the CCE estimator is obtained.
The common factors are extracted using the principal component analysis.
Empirical studies show that realized volatility models exploiting the linkage
effects lead to signiﬁcantly better out-of-sample forecast performance, for
example, an up to 32% increase in the pseudo R2. We also conduct various
forecasting exercises on the linkage variables that compare conventional
regression methods with popular machine learning techniques.