The contribution of realized variance–covariance models to the economic value of volatility timing

Abstract

Realized variance–covariance models define the conditional expectation of a realized variance–covariance matrix as a function of past matrices using a GARCH-type structure. We evaluate the forecasting performance of various models in terms of economic value, measured through economic loss functions, across two datasets. Our empirical findings reveal that models incorporating realized volatilities offer significant economic value and outperform GARCH models relying solely on daily returns for daily and weekly horizons. Among two realized variance–covariance measures, using a directly rescaled intraday measure for full-day estimation provides more economic value than employing overnight returns, which tends to produce noisier estimators of overnight covariance, diminishing their predictive effectiveness. The HEAVY-H model for the variance–covariance matrix of the daily return demonstrates superior or comparable performance to the best-performing realized variance–covariance models, making it a preferred choice for empirical analysis.