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Multivariate semi-nonparametric distributions with dynamic conditional correlations

  • Del Brio, Esther B.
  • Ñíguez, Trino-Manuel
  • Perote, Javier

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.

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Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 27 (2011)
Issue (Month): 2 ()
Pages: 347-364

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Handle: RePEc:eee:intfor:v:27:y:2011:i:2:p:347-364
Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

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