补充附录:估计随机变量线性组合偏度的高斯偏度收缩法

Kris Boudt, D. Cornilly, Tim Verdonck
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引用次数: 0

摘要

全文可在https://ssrn.com/abstract=2839781在论文的补充附录中,Boudt, Cornilly, and Verdonck(2018)讨论了自相关和时变结构对余偏性矩阵估计的影响,并提供了有关结构化估计的更多解释。我们将更详细地介绍我们优化目标的方法,并提供文献中其他余偏性矩阵的几个示例。特别是,我们提供了如何在收缩设置中使用Simaan(1993)的潜在单因素协方差矩阵的指导,并纠正了Martellini和Ziemann(2010)中提供的估计量,以估计C(Φ,T)为其观察到的单因素和恒定相关的协方差估计量。本文对主要论文中讨论的仿真设置进行了更详细的探讨,并研究了另外三个数据生成过程。此外,我们还详细介绍了主论文中研究的实证应用,并扩展了Martellini和Ziemann(2010)的实证研究。最后,我们介绍了R代码,在Peterson和Carl(2018)的PerformanceAnalytics包中公开提供,用于所有单目标和多目标收缩估计器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Supplementary Appendix to: A Coskewness Shrinkage Approach for Estimating the Skewness of Linear Combinations of Random Variables
Full Paper is available at: https://ssrn.com/abstract=2839781 In the supplementary appendix to the paper Boudt, Cornilly, and Verdonck (2018) we discuss the impact of autocorrelation and a time-varying structure on the estima- tion of coskewness matrices and provide more explanation about structured estima- tion. We go into more detail about our approach of optimizing the targets and provide several examples of other coskewness matrices in the literature. In particular, we pro- vide guidance how to use the latent single-factor coskewness matrix of Simaan (1993) in a shrinkage setting and correct the estimators provided in Martellini and Ziemann (2010) to estimate C(Φ ,T) for their observed single-factor and constant correlation coskewness estimators. The simulation setting discussed in the main paper is explored into more detail and we study three additional data generating processes. Further- more, we provide details on the empirical application studied in the main paper and extend the empirical study of Martellini and Ziemann (2010). Finally, we introduce the R code, publicly available in the PerformanceAnalytics package of Peterson and Carl (2018), for all single- and multi-target shrinkage estimators.
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