Bayesian Mixture Copula Estimation and Selection with Applications

Yujian Liu, Dejun Xie, Siyi Yu
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引用次数: 3

Abstract

Mixture copulas are popular and essential tools for studying complex dependencies among variables. However, selecting the correct mixture models often involves repeated testing and estimations using criteria such as AIC, which could require effort and time. In this paper, we propose a method that would enable us to select and estimate the correct mixture copulas simultaneously. This is accomplished by first overfitting the model and then conducting the Bayesian estimations. We verify the correctness of our approach by numerical simulations. Finally, the real data analysis is performed by studying the dependencies among three major financial markets.
贝叶斯混合Copula估计与选择及其应用
混合copula是研究变量间复杂依赖关系的重要工具。然而,选择正确的混合模型通常涉及使用AIC等标准的重复测试和估计,这可能需要精力和时间。在本文中,我们提出了一种能够同时选择和估计正确的混合copula的方法。这是通过首先过拟合模型,然后进行贝叶斯估计来完成的。通过数值模拟验证了该方法的正确性。最后,通过研究三大金融市场之间的依赖关系进行真实数据分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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