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Multivariate semi-nonparametric distributions with dynamic conditional correlations
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DOI：10.1016/j.ijforecast.2010.02.005.png)
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Abstract
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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. (C) 2010 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keywords:
Density forecasts
Financial markets
GARCH models
Multivariate time series
Semi-nonparametric methods
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