英文标题：
《Multivariate Geometric Expectiles》
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作者：
Klaus Herrmann, Marius Hofert, Melina Mailhot
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最新提交年份：
2018
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英文摘要：
A generalization of expectiles for d-dimensional multivariate distribution functions is introduced. The resulting geometric expectiles are unique solutions to a convex risk minimization problem and are given by d-dimensional vectors. They are well behaved under common data transformations and the corresponding sample version is shown to be a consistent estimator. We exemplify their usage as risk measures in a number of multivariate settings, highlighting the influence of varying margins and dependence structures.
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中文摘要：
介绍了d维多元分布函数期望值的一种推广。由此产生的几何期望值是凸风险最小化问题的唯一解，由d维向量给出。它们在常见的数据转换下表现良好，相应的样本版本被证明是一致的估计量。我们举例说明了它们在多变量环境中作为风险度量的使用，强调了不同利润率和依赖结构的影响。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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