Robust Forecasting of Dynamic Conditional Correlation GARCH Models

37 Pages Posted: 30 Nov 2010 Last revised: 30 Aug 2013

See all articles by Kris Boudt

Kris Boudt

Ghent University; Vrije Universiteit Brussel; Vrije Universiteit Amsterdam

Jon Danielsson

London School of Economics - Systemic Risk Centre

Sébastien Laurent

AMSE

Date Written: April 18, 2012

Abstract

Large once-off events cause large changes in prices but may not affect volatility and correlation dynamics as much as smaller events. Standard volatility models may deliver biased covariance forecasts in this case. We propose a multivariate volatility forecasting model that is accurate in the presence of large once-off events. The model is an extension of the dynamic conditional correlation model (DCC) model. Compared to the DCC model, our method produces more precise out-of-sample covariance forecasts and, when used in portfolio allocation, it leads to portfolios with similar return characteristics but lower turnover and hence higher profits.

Suggested Citation

Boudt, Kris and Danielsson, Jon and Laurent, Sébastien, Robust Forecasting of Dynamic Conditional Correlation GARCH Models (April 18, 2012). International Journal of Forecasting 29, 244-257. Available at SSRN: https://ssrn.com/abstract=1717796 or http://dx.doi.org/10.2139/ssrn.1717796

Kris Boudt (Contact Author)

Ghent University ( email )

Sint-Pietersplein 5
Gent, 9000
Belgium

Vrije Universiteit Brussel ( email )

Pleinlaan 2
http://www.vub.ac.be/
Brussels, 1050
Belgium

Vrije Universiteit Amsterdam ( email )

De Boelelaan 1105
Amsterdam, ND North Holland 1081 HV
Netherlands

Jon Danielsson

London School of Economics - Systemic Risk Centre ( email )

Houghton Street
London WC2A 2AE
United Kingdom
+44.207.955.6056 (Phone)

HOME PAGE: http://www.riskreasearch.org

Sébastien Laurent

AMSE ( email )

2 rue de la Charité
Marseille, 13236
France

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