Title | Multivariate Semi-nonparametric Distributions with Dynamic Conditional Correlations |
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Type | Journal article |
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Authors | Del Brio, E.B., Ñíguez, T.M. and Perote, J. |
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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. |
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Keywords | Density forecasts; Financial markets; GARCH models; Multivariate time series; Semi-nonparametric methods. |
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Journal | International Journal of Forecasting |
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Journal citation | 27 (2), pp. 347-364 |
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ISSN | 0169-2070 |
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Year | 2011 |
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Publisher | Elsevier |
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Digital Object Identifier (DOI) | https://doi.org/10.1016/j.ijforecast.2010.02.005 |
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Publication dates |
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Published | 01 Sep 2010 |
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Published | Apr 2011 |
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