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http://dx.doi.org/10.5351/KJAS.2009.22.5.995

Analysis of Multivariate-GARCH via DCC Modelling  

Choi, S.M. (Department of Statistics, Sookmyung Women's University)
Hong, S.Y. (Department of Statistics, Sookmyung Women's University)
Choi, M.S. (Department of Statistics, Sookmyung Women's University)
Park, J.A. (Department of Statistics, Sookmyung Women's University)
Baek, J.S. (Department of Statistics, Sookmyung Women's University)
Hwang, S.Y. (Department of Statistics, Sookmyung Women's University)
Publication Information
The Korean Journal of Applied Statistics / v.22, no.5, 2009 , pp. 995-1005 More about this Journal
Abstract
Conditional correlation between financial time series plays an important role in risk management, asset allocation and portfolio selection and therefore diverse efforts for modeling conditional correlations in multivariate-GARCH processes have been made in last two decades. In particular, CCC (cf. Bollerslev, 1990) and DCC(dynamic conditional correlation, cf. Engle, 2002) models have been commonly used since they are relatively parsimonious in the number of parameters involved. This article is concerned with DCC modeling for multivariate GARCH processes in comparison with CCC specification. Various multivariate financial time series are analysed to illustrate possible advantages of DCC over CCC modeling.
Keywords
Multivariate-GARCH; CCC(constant conditional correlation); DCC(dynamic conditional correlation);
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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