Vine-Copula GARCH Model with Dynamic Conditional Dependence

2013 ◽  
Author(s):  
Mike K. P. So ◽  
Cherry Y.T. Yeung
Keyword(s):  
Garch Model ◽  
Conditional Dependence ◽  
Vine Copula

scholarly journals Assessing Efficiency of D-Vine Copula ARMA-GARCH Method in Value at Risk Forecasting: Evidence from PSE Listed Companies

2015 ◽  
Vol 63 (4) ◽  
pp. 1287-1295 ◽  
Author(s):  
Václav Klepáč ◽  
David Hampel
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange ◽  
Garch Model ◽  
Dependence Structure ◽  
Process Innovations ◽  
Out Of Sample ◽  
Copula Approach ◽  
Multivariate Probability Distribution ◽  
Vine Copula

The article points out the possibilities of using static D-Vine copula ARMA-GARCH model for estimation of 1 day ahead market Value at Risk. For the illustration we use data of the four companies listed on Prague Stock Exchange in range from 2010 to 2014. Vine copula approach allows us to construct high-dimensional copula from both elliptical and Archimedean bivariate copulas, i.e. multivariate probability distribution, created from process innovations. Due to a deeper shortage of existing domestic results or comparison studies with advanced volatility governed VaR forecasts we backtested D-Vine copula ARMA-GARCH model against the VaR rolling out of sample forecast from October 2012 to April 2014 of chosen benchmark models, e.g. multivariate VAR-GO-GARCH, VAR-DCC-GARCH and univariate ARMA-GARCH type models. Common backtesting via Kupiec and Christoffersen procedures offer generalization that technological superiority of model supports accuracy only in case of an univariate modeling – working with non-basic GARCH models and innovations with leptokurtic distributions. Multivariate VAR governed type models and static Copula Vines performed in stated backtesting comparison worse than selected univariate ARMA-GARCH, i.e. it have overestimated the level of actual market risk, probably due to hardly tractable time-varying dependence structure.

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