Library Header Image
LSE Research Online LSE Library Services

Robust forecasting of dynamic conditional correlation GARCH models

Boudt, Kris, Danielsson, Jon and Laurent, Sebastien (2013) Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29 (2). pp. 244-257. ISSN 0169-2070

Full text not available from this repository.

Identification Number: 10.1016/j.ijforecast.2012.06.003


Large one-off events cause large changes in prices, but may not affect the volatility and correlation dynamics as much as smaller events. In such cases, standard volatility models may deliver biased covariance forecasts. We propose a multivariate volatility forecasting model that is accurate in the presence of large one-off events. The model is an extension of the dynamic conditional correlation (DCC) model. In our empirical application to forecasting the covariance matrix of the daily EUR/USD and Yen/USD return series, we find that our method produces more precise out-of-sample covariance forecasts than the DCC model. Furthermore, when used in portfolio allocation, it leads to portfolios with similar return characteristics but lower turnovers, and hence higher profits.

Item Type: Article
Official URL:
Additional Information: © 2012 International Institute of Forecasters
Divisions: Finance
Financial Markets Group
Subjects: H Social Sciences > HG Finance
Date Deposited: 05 Mar 2013 12:50
Last Modified: 25 Jun 2024 20:42
Funders: Dutch Science Foundation

Actions (login required)

View Item View Item