Del, Brio E. B., Níguez, T. M. and Perote, J. (2010) The SNP-DCC model: a new methodology for risk management and forecasting. Working Paper. Fundación de las Cajas de Ahorros.Full text not available from this repository.
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|
|Subjects:||University of Westminster > Westminster Business School|
|Date Deposited:||02 Nov 2012 16:45|
|Last Modified:||18 Mar 2016 16:51|
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