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用半参数阿基米德Copula函数分析财务数据中模型的相关性
Modeling Dependence in Financial Data with Semiparametric Archimedean Copulas |
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| 课程网址: |
http://videolectures.net/amlcf09_lobato_mdfd/
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| 主讲教师: |
José Miguel Hernández-Lobato
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| 开课单位: |
马德里自治大学
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| 开课时间: |
信息不详。欢迎您在右侧留言补充。
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| 课程语种: |
英语
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| 中文简介: |
Copulas是构建多变量模型的有用工具，因为它允许将单变量边缘连接到具有任意依赖结构的联合模型中。虽然非参数关联模型的泛化性能较差，但标准的参数关联模型通常缺乏表达能力来捕获财务数据中存在的依赖关系。在这项工作中，我们提出了一个新的半参数二元阿基米德连合模型，表示为一个潜在的函数。该隐函数采用自然样条逼近，模型参数采用最大惩罚似然选择。利用金融数据的实验来评价所提估计器相对于其他基准方法的准确性:文献中介绍的阿基米德copulas的两个灵活估计器、考虑更一般依赖关系的两种copulas估计方法和三个参数copulas模型。该半参数关联模型具有良好的样本内和样本外性能，是多变量金融数据建模的有效工具。
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| 课程简介: |
Copulas are useful tools for the construction of multivariate models because they allow to link univariate marginals into a joint model with arbitrary dependence structure. While non-parametric copula models can have poor generalization performance, standard parametric copulas often lack expressive capacity to capture the dependencies present in financial data. In this work, we propose a novel semiparametric bivariate Archimedean copula model that is expressed in terms of a latent function. This latent function is approximated using a basis of natural splines and the model parameters are selected by maximum penalized likelihood. Experiments on financial data are used to evaluate the accuracy of the proposed estimator with respect to other benchmark methods: Two flexible estimators of Archimedean copulas previously introduced in the literature, two approaches for copula estimation that allow for more general dependencies and three parametric copulas models. The proposed semiparametric copula model has excellent in and out-of-sample performance, which makes it a useful tool for modeling multivariate financial data.
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| 关 键 词: |
Copula函数; 财务数据; 模型相关性
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-10-30：cwx
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| 阅读次数: |
49
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