Factor High-Frequency-Based Volatility (HEAVY) Models

We propose a new class of multivariate volatility models utilizing realized measures of asset variance and covariance extracted from high-frequency data. Dimension reduction for estimation of large covariance matrices is achieved by imposing a factor structure with time-varying conditional factor loadings. Statistical properties of the model, including conditions that ensure covariance stationarity of returns, are established. The performance of the model is assessed using a panel of large U.S. financial institutions during the financial crisis, where empirical results show that the new model has both superior in- and out-of-sample properties. We show that the superior performance applies to a wide range of quantities of interest, including volatilities, covariances, betas, and scenario-based risk measures. The model’s performance is particularly strong at short forecast horizons.