Improving the Forecasting of Dynamic Conditional Correlation: a Volatility Dependent Approach

Edoardo Otranto

Working Paper CRENoS from Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia

Abstract: Forecasting volatility in a multivariate framework has received many contributions in the recent literature, but problems in estimation are still frequently encountered when dealing with a large set of time series. The Dynamic Conditional Correlation (DCC) modeling is probably the most used approach; it has the advantage of separating the estimation of the volatility of each time series (with great flexibility, using single univariate models) and the correlation part (with the strong constraint imposing the same dynamics to all the correlations). We propose a modification to the DCC model, providing different dynamics for each correlation, simply hypothesizing a dependence on the volatility structure of each time series. This new model implies adding only two parameters with respect to the original DCC model. Its performance is evaluated in terms of out-of-sample forecasts with respect to the DCC models and other multivariate GARCH models. The results on four data sets seem to favor the new model.

Keywords: dynamic conditional correlation; garch distance; multivariate (search for similar items in EconPapers)
JEL-codes: C32 C53 G10 (search for similar items in EconPapers)
Date: 2009
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:cns:cnscwp:200917

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