期刊名称：International Journal of Economics and Finance
印刷版ISSN：1916-971X
电子版ISSN：1916-9728
出版年度：2014
卷号：2
期号：5
页码：2
DOI：10.5539/ijef.v2n5p2
语种：English
出版社：Canadian Center of Science and Education
摘要：We present a new recursive algorithm to construct vine copulas based on an underlying tree structure. This new structure is interesting to compute multivariate distributions for dependent random variables. We prove the asymptotic normality of the vine copula parameter estimator and show that all vine copula parameter estimators have comparable variance. Both results are crucial to motivate any econometrical work based on vine copulas. We provide an application of vine copulas to estimate the VaR of a portfolio, and show they offer significant improvement as compared to a benchmark estimator based on a GARCH model.
