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