Multivariate semi-nonparametric distributions with dynamic conditional correlations

• Published in: International journal of forecasting Vol. 27; no. 2; pp. 347 - 364
• Main Authors: Del Brio, Esther B., Ñíguez, Trino-Manuel, Perote, Javier
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.04.2011; Elsevier; Elsevier Sequoia S.A
• Series: International Journal of Forecasting
• Subjects: Correlation analysis; Density forecasts; Density forecasts Financial markets GARCH models Multivariate time series Semi-nonparametric methods; Estimating techniques; Financial markets; Forecasting; GARCH models; Multivariate analysis; Multivariate time series; Rates of return; Semi-nonparametric methods; Studies; GARCH models; Multivariate time series; Semi-nonparametric methods; Financial markets; Density forecasts
• ISSN: 0169-2070; 1872-8200
• DOI: 10.1016/j.ijforecast.2010.02.005

This paper generalizes the Dynamic Conditional Correlation (DCC) model of Engle (2002), incorporating a flexible non-Gaussian distribution based on Gram-Charlier expansions. The resulting semi-nonparametric-DCC (SNP-DCC) model allows estimation in two stages and deals with the negativity problem which is inherent in truncated SNP densities. We test the performance of a SNP-DCC model with respect to the (Gaussian)-DCC through an empirical application of density forecasting for portfolio returns. Our results show that the proposed multivariate model provides a better in-sample fit and forecast of the portfolio returns distribution, and thus is useful for financial risk forecasting and evaluation.