Multivariate semi-nonparametric distributions with dynamic conditional correlations

Esther Del Brio (), Trino Ñíguez Grau and Javier Perote

International Journal of Forecasting, 2011, vol. 27, issue 2, 347-364

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-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.

Keywords: Density; forecasts; Financial; markets; GARCH; models; Multivariate; time; series; Semi-nonparametric; methods (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:27:y::i:2:p:347-364

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