Modelling Co-movements and Tail Dependency in the International Stock Market via Copulae
Modelling Co-movements and Tail Dependency in the International Stock Market via Copulae
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DOI：
10.2139/ssrn.2170214
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发表时间：
2009-12
影响因子：
1.7
通讯作者：
Katja Ignatieva;E. Platen
中科院分区：
文献类型：
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作者：
Katja Ignatieva;E. Platen
本文使用时变系词研究了国际股票市场的联动。我们检查对称广义双曲 (SGH) 分布类别的分布，以对股票指数回报的单变量边际进行建模。我们基于拟合优度检验表明，SGH 类优于正态分布，并且边际上的 Student-t 假设导致最佳性能，因此可用于拟合股票指数回报联合分布的多元联结函数。我们在研究中表明，Student-t copula 不仅优于高斯 copula（其中依赖结构与多元正态分布相关），而且优于一些替代混合 copula 模型，这些模型允许反映分布尾部的不对称依赖性。 Student-t 联结函数与 Student-t 边际允许对真实的同步联动进行建模，并捕获股票指数回报中的尾部依赖性。从风险管理的角度来看，它是对国际股票指数投资组合中产生的回报进行建模的良好候选者，其中已知极端损失倾向于同时发生。我们将 copula 应用于风险价值和预期缺口的估计，并表明具有 Student-t 边际的 Student-t copula 优于所研究的替代 copula 模型以及风险计量方法。
This paper examines international equity market co-movements using time-varying copulae. We examine distributions from the class of Symmetric Generalized Hyperbolic (SGH) distributions for modelling univariate marginals of equity index returns. We show based on the goodness-of-fit testing that the SGH class outperforms the normal distribution, and that the Student-t assumption on marginals leads to the best performance, and thus, can be used to fit multivariate copula for the joint distribution of equity index returns. We show in our study that the Student-t copula is not only superior to the Gaussian copula, where the dependence structure relates to the multivariate normal distribution, but also outperforms some alternative mixture copula models which allow to reflect asymmetric dependencies in the tails of the distribution. The Student-t copula with Student-t marginals allows to model realistically simultaneous co-movements and to capture tail dependency in the equity index returns. From the point of view of risk management, it is a good candidate for modelling the returns arising in an international equity index portfolio where the extreme losses are known to have a tendency to occur simultaneously. We apply copulae to the estimation of the Value-at-Risk and the Expected Shortfall, and show that the Student-t copula with Student-t marginals is superior to the alternative copula models investigated, as well the Riskmetics approach.