Range-Based DCC Models for Covariance and Value-at-Risk Forecasting

Piotr Fiszeder · Marcin Fałdziński · Péter Molnár

59 citations0/0 files storedJournal of Empirical Finance

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Market Dynamics and Volatility

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.

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References

1991Conditional Heteroskedasticity in Asset Returns: A New Approach 1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks 2002Dynamic Conditional Correlation 1994Comparing Predictive Accuracy 1995Comparing Predictive Accuracy 2003Modeling and Forecasting Realized Volatility 1998Evaluating Interval Forecasts 1995Techniques for Verifying the Accuracy of Risk Measurement Models

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