The structure of high-dimensional dynamic vine copula can overcome ‘dimensional curse’ faced by bivariate Copula and dynamically describe nonlinear dependence between multi-variables, and represents the academic frontier. Five kinds of foreign exchange log-returns, including USD, EUR, JPY, HKD and GBP, are selected to make empirical analysis, Time series are fitted with AR-GJR-GARCH and GPD models. After probability integral transform, high-dimensional dynamic C and D vine copulas are modelled. Then, portfolio VaR sets are got by Monte-Carlo method, tested by UC back testing, and compared to the corresponding static research. The results show that VaR based on high-dimensional dynamic C vine copula performs the best, and marginal risk of USD is considered as the least by VaR decomposition, the more USD the lest risk. The conclusions provide a new model and method to quantify risk, reasonably allocate asset portfolio, and for authorities to regulate risk.
HAN Chao, YAN Tai-hua
. Risk analysis of Foreign Exchange Portfolios Based on High-dimensional Dynamic Vine Copula[J]. Chinese Journal of Management Science, 2017
, 25(2)
: 10
-20
.
DOI: 10.16381/j.cnki.issn1003-207x.2017.02.002
[1] 吴振翔,叶五一,缪柏其.基于Copula的外汇投资组合风险分析[J].中国管理科学,2004,12(4):1-5.
[2] Patton A J. Modelling asymmetric exchange rate dependence[J].International Economic Review,2006,47(2):527-556.
[3] 柏满迎,孙禄杰.三种Copula-VaR计算方法与传统VaR方法的比较[J].数量经济技术经济研究, 2007,(02):154-160.
[4] 苟红军,陈迅,花拥军.基于GARCH-EVT-COPULA模型的外汇投资组合风险度量研究[J].管理工程学报,2015,9(1):83-193.
[5] So M K P,Yeung C Y T. Vine-copula GARCH model with dynamic conditional dependence[J].Computational Statistics and Data Analysis,2014,76:655-671.
[6] Reboredoa J C, Andrea U. Downside/upside price spillovers between precious metals:A vine copula approach[J].North American Journal of Economics and Finance,2015,34:84-102.
[7] 叶五一,李潇颖,缪柏其.基于藤Copula方法的持续期自相依结构估计及预测[J].中国管理科学,2015,23(11):29-38.
[8] 杜子平,闫鹏,张勇.基于"藤"结构的高维动态copula的构建[J].数学的实践与认识,2009,39(10):96-102.
[9] Sklar A. Fonctions de répartition àn dimentions et leursmarges[J].Publication de I'Institut de Statistique de L'Universit-é de Paris:1959,8:229-231.
[10] Aas K,Czado C,Frigessi A,et al. Pair-copula constructions of multiple dependence[J].Insurance:Mathematics & Economics, 2009,44(2):182-198.
[11] Kupiec P.Techniques for verifying the accuracy of risk measurement models[J]. Journal of Derivatives,1995,3:73-84.
[12] Brock W, Dechert W, Scheinkman J.A test for independence based on the correlation dimension[R].Working Paper,University of Wisconsin at Madison, University of Houston,and University of Chicago,1987.
[13] Mcneil A J,Frey R.Estimation of tail-related risk measures for heteroscedastic financial time series:An extreme value approach[J]. Journal of Empirical Finance,1998,7(3-4):271-300.
[14] Pickands J. Statistical inference using extreme order statistics[J].The Ann als of Statistics, 1975,3(1):119-131.
[15] 韦艳华,张世英.Copula理论及其在金融分析上的应用[M].北京:清华大学出版社,2008.
[16] Hallerbach W G. Decomposing portfolio value at risk:A general analysis[J]. Journal of Risk,2003,5(2):1-18.
[17] 杜红军,王宗军. 基于Copula-AL法的VaR和CVaR的度量与分配[J].中国管理科学,2012, 20(3):1-9.
[18] 胡海鹏,方兆本.投资组合VaR及其分解[J]. 中国管理科学,2003,11(3):1-5.
[19] 邵欣炜,张屹山. 基于VaR的证券投资组合风险评估及管理体系[J].数量经济技术经济研究,2003,12:66-70.
