Range-based DCC models for covariance and value-at-risk forecasting

Abstract

The dynamic conditional correlation (DCC) model by Engle (2002) is one of the most popular multivariate volatility models. This model is based solely on closing prices. It has been documented in the literature that the high and low prices of a given day can be used to obtain an efficient volatility estimation. We therefore suggest a model that incorporates high and low prices into the DCC framework. We conduct an empirical evaluation of this model on three datasets: currencies, stocks, and commodity exchange traded funds. Regardless of whether we consider in-sample fit, covariance forecasts or value-at-risk forecasts, our model outperforms not only the standard DCC model, but also an alternative range-based DCC model.