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Copula方法中的边缘分布设定的计量检验
Dose Marginal Distribution Require to Be Tested in Copula Method? An Asset Allocation Perspective Based on CVaR Framework
Copula 理论已经明确了边缘分布需要正确的设定, 而在实证应用中, 边缘分布的设定检验存在着改进的空间, 会使模 型风险下降. 与以往拆开检验不同, 选择最新的统计量同时检验 边缘分布的设定, Monte Carlo 模拟结果显示同时检验比拆开检 验更加稳健. 通过资产组合的实证分析, 发现通过检验的边缘分布在样本内获得更高的有效前沿, 样本外的测试表明正确的边缘分布设定可以得到更好的投资绩效, 显著性检验也支持了该结论.
Marginal distribution requires to be tested in term of Copula theory, but the test can be improved in empirical analysis, which reduces the risks of model. Unlike testing apart, we use a new simultaneous statistic for testing the marginal distribution, Monte Carlo results show that our method is more robust than testing apart. From asset allocation analysis, we find that the marginal distribution which passes the test can obtain higher efficient frontier, and get a better out-of-sample performance. The significant test supports our conclusions.
Copula 方法 / 边缘分布检验 / CVaR / 资产组合. {{custom_keyword}} /
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