Abstract
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.
Highlights
Models that can describe the dynamic properties of two or more asset returns play an important role in financial econometrics
We propose a dynamic conditional correlation (DCC) model constructed using the Range-GARCH model by Molnár (2016), which is formulated with the usage of low and high prices and based on returns calculated from closing prices
We show using low and high prices in the formulation of the DCC model improves the estimation of the covariance matrix of returns and increases the accuracy of covariance and VaR forecasts based on this model, compared with the standard DCC model based on closing prices
Summary
Models that can describe the dynamic properties of two or more asset returns play an important role in financial econometrics. We propose a DCC model constructed using the Range-GARCH model by Molnár (2016), which is formulated with the usage of low and high prices and based on returns calculated from closing prices. We show using low and high prices in the formulation of the DCC model improves the estimation of the covariance matrix of returns and increases the accuracy of covariance and VaR forecasts based on this model, compared with the standard DCC model based on closing prices. Since both models, DCC and DCC-RGARCH, share the same structure in the correlation component, achieving more precise volatility estimates improves the covariance forecasts.
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