以Pair Copula为简单构造模块的高维动态藤Copula结构能够克服二元Copula面临的“维度诅咒”问题，对多元变量之间的非线性相依进行动态化描述，是Copula函数研究的学术前沿。本文选取美元、欧元、日元、港币及英镑五种汇率的日间对数收益率数据实证研究，对其进行AR-GJR-GARCH模型过滤，过滤所得新息序列用GPD模型拟合，之后进行概率积分变换，采用高维动态C藤和D藤Copula对变换后序列建模，运用蒙特卡罗方法计算组合风险VaR，对其进行UC回溯测试，并与相应的静态方法作比较。结果表明：高维动态C藤Copula结构计算出来的VaR表现最好，对其进行分解发现美元的边际风险最低，通过蒙特卡罗选择权重组合发现最大限度持有美元将会产生最小VaR。该结论为量化风险指标、合理配置资产，及风险监管提供了一种新的模型与方法。
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.
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