DETECTING FINANCIAL DATA DEPENDENCE STRUCTURE BY AVERAGING MIXTURE COPULAS
DETECTING FINANCIAL DATA DEPENDENCE STRUCTURE BY AVERAGING MIXTURE COPULAS
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DOI：
10.1017/s0266466618000270
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发表时间：
2018-09
影响因子：
0.8
通讯作者：
Guannan Liu;Wei Long;Xinyu Zhang;Qi Li
中科院分区：
文献类型：
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作者：
Guannan Liu;Wei Long;Xinyu Zhang;Qi Li
混合联结是几个单独联结的线性组合，可以用来产生不属于现有联结族的依赖结构。由于不同的市场对在实证研究中可能表现出截然不同的依赖结构，因此混合联结在建模金融数据的依赖性方面是有用的。因此，我们建议使用模型平均方法来估计混合copula框架中的金融数据依赖结构，而不是基于某些标准选择单个copula。我们通过j折交叉验证程序选择权重（用于平均）。我们证明了模型平均估计量是渐近最优的，因为它使估计损失的平方最小。仿真结果表明，在工作混合模型不确定的情况下，模型平均方法优于其他方法。利用四个发达经济体股票指数12年的日收益数据，我们表明模型平均方法比一些竞争方法更准确地估计了它们的依赖结构。
A mixture copula is a linear combination of several individual copulas that can be used to generate dependence structures not belonging to existing copula families. Because different pairs of markets may exhibit quite different dependence structures in empirical studies, mixture copulas are useful in modeling the dependence in financial data. Therefore, rather than selecting a single copula based on certain criteria, we propose using a model averaging approach to estimate financial data dependence structures in a mixture copula framework. We select weights (for averaging) by a J-fold Cross-Validation procedure. We prove that the model averaging estimator is asymptotically optimal in the sense that it minimizes the squared estimation loss. Our simulation results show that the model averaging approach outperforms some competing methods when the working mixture model is misspecified. Using 12 years of data on daily returns from four developed economies’ stock indexes, we show that the model averaging approach more accurately estimates their dependence structures than some competing methods.