Multivariate Semi-nonparametric Distributions with Dynamic Conditional Correlations

Del Brio, E.B., Ñíguez, T.M. and Perote, J. 2011. Multivariate Semi-nonparametric Distributions with Dynamic Conditional Correlations. International Journal of Forecasting. 27 (2), pp. 347-364. https://doi.org/10.1016/j.ijforecast.2010.02.005

TitleMultivariate Semi-nonparametric Distributions with Dynamic Conditional Correlations
TypeJournal article
AuthorsDel Brio, E.B., Ñíguez, T.M. and Perote, J.
Abstract

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 (SNP)-DCC model admits estimation in two stages and deals with the negativity problem inherent to 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, being thus useful for financial risk forecasting and evaluation.

KeywordsDensity forecasts; Financial markets; GARCH models; Multivariate time series; Semi-nonparametric methods.
JournalInternational Journal of Forecasting
Journal citation27 (2), pp. 347-364
ISSN0169-2070
Year2011
PublisherElsevier
Digital Object Identifier (DOI)https://doi.org/10.1016/j.ijforecast.2010.02.005
Publication dates
Published01 Sep 2010
PublishedApr 2011

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