Improving forecasts with the co-range dynamic conditional correlation model

Abstract

We introduce a new specification of the dynamic conditional correlation (DCC) model, where its parameters are estimated with the use of closing and additionally low and high prices. Such prices are often commonly available for many financial series and contain more information about the variation of returns. We construct the model with the range-based estimator of variance but more importantly also with the range-based estimator of covariance. The latter estimator and as a consequence the proposed DCC model require, however, that the range of a portfolio return is given. We compare the model with three other specifications of the DCC models and evaluate them based on Monte Carlo experiments and currencies rates from the Forex market. We show that the use of low and high prices can improve estimation of covariance and correlation matrices of returns and increase the accuracy of forecasts of covariance and correlation matrices based on this model, compared with using closing prices only. The proposed model is superior not only to the standard DCC model, but also to the competing range-based DCC model.