Article2013

Dynamic Conditional Correlation: On Properties and Estimation

423 citations0/0 files storedJournal of Business and Economic Statistics

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Complex Systems and Time Series Analysis

Abstract

This article addresses some of the issues that arise with the Dynamic Conditional Correlation (DCC) model. It is proven that the DCC large system estimator can be inconsistent, and that the traditional interpretation of the DCC correlation parameters can result in misleading conclusions. Here, we suggest a more tractable DCC model, called the cDCC model. The cDCC model allows for a large system estimator that is heuristically proven to be consistent. Sufficient stationarity conditions for cDCC processes of interest are established. The empirical performances of the DCC and cDCC large system estimators are compared via simulations and applications to real data.

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References

1986Generalized autoregressive conditional heteroskedasticity 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1991Conditional Heteroskedasticity in Asset Returns: A New Approach 2002Dynamic Conditional Correlation 1994Comparing Predictive Accuracy 1995Comparing Predictive Accuracy 1995Multivariate Simultaneous Generalized ARCH 1990Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized Arch Model

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