Copula-based measures of dependence structure in assets returns
Copula-based measures of dependence structure in assets returns
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DOI：
10.1016/j.physa.2008.02.055
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发表时间：
2008-06
影响因子：
3.3
通讯作者：
V. Fernandez
中科院分区：
文献类型：
--
作者：
V. Fernandez
Copula模型在金融领域已成为一种日益流行的资产回报依赖性建模工具。从本质上讲，copula使我们能够从一组随机变量的联合分布函数中提取出依赖结构，同时将这种依赖结构从单变量的边际行为中分离出来。在本研究中，基于美国股票数据，我们说明了尾部依赖测试作为选择密切模仿数据依赖结构的联结的工具可能会产生误导。当数据通过条件波动率缩放和/或过滤出序列相关性时，这个问题变得更加严重。在更一般的情况下，讨论得到了蒙特卡罗模拟和投资组合管理含义的补充。
Copula modeling has become an increasingly popular tool in finance to model assets returns dependency. In essence, copulas enable us to extract the dependence structure from the joint distribution function of a set of random variables and, at the same time, to isolate such dependence structure from the univariate marginal behavior. In this study, based on US stock data, we illustrate how tail-dependency tests may be misleading as a tool to select a copula that closely mimics the dependency structure of the data. This problem becomes more severe when the data is scaled by conditional volatility and/or filtered out for serial correlation. The discussion is complemented, under more general settings, with Monte Carlo simulations and portfolio management implications.