Del Brio, Esther B. and Níguez, Trino Manuel and Perote, Javier (2010) The SNP-DCC model: a new methodology for risk management and forecasting. Working Paper. Fundación de las Cajas de Ahorros.
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This paper generalizes the Dynamic Conditional Correlation (DCC) model of En- gle (2002) to incorporate a flexible non-Gaussian distribution based on Gram-Charlier expansions. The resulting semi-nonparametric (SNP)-DCC model admits a separate estimation of, in a first stage, the individual conditional variances under a Gaussian distribution and, in the second stage, the conditional correlations and the rest of the density parameters, thus overcoming the known "dimensionality curse" of the mul- tivariate volatility models. Furthermore the proposed SNP-DCC model solves the negativity problem inherent to truncated SNP densities providing a parametric struc- ture that may accurately approximate a target heavy-tailed distribution. We test the performance of a SNP-DCC model with respect to the (Gaussian)-DCC through an empirical application of density forecasting for portfolio asset returns data. 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.
|Item Type:||Monograph (Working Paper)|
|Additional Information:||Working Paper No. 532|
|Research Community:||University of Westminster > Westminster Business School|
|Deposited On:||02 Nov 2012 16:45|
|Last Modified:||02 Nov 2012 16:46|
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