跳至正文
返回文库全部文档

估算最小均值—方差有效前沿张成集

文章 arXiv papers · 作者: Zhipeng Liao et al.
阅读原文

总结

本文研究如何识别一个较大资产集合中能够张成其均值—方差有效前沿的最小风险资产子集。文章提出识别该子集的条件，并介绍一种估计与推断程序，旨在以趋近于一的概率覆盖真实集合，并达到所选置信水平对应的收敛保证。

文中将蒙特卡洛模拟作为有限样本表现良好的证据。实证应用将个股动量和因子动量策略与成熟的股票收益因子进行比较。研究发现，因子动量、部分个股动量策略以及若干收益因子是均值—方差有效性的重要贡献因素；分析还考察并对这些贡献进行排序。文中未提供样本、具体资产、估计选择或稳健性检验的细节，因此无法超出这份概述评估其实证结论。

核心观点

  • 最小张成集是能够张成投资组合资产集合均值—方差有效前沿的最小风险资产子集。
  • 识别条件界定了何时能够找出这一子集。
  • 所提出的方法估计该集合，并提供渐近覆盖和收敛保证。
  • 研究使用蒙特卡洛模拟评估有限样本表现。
  • 一项应用对个股动量、因子动量和成熟收益因子对均值—方差有效性的贡献进行排序。

标签

因子投资动量投资组合构建统计学
全文
# Testing for the Minimum Mean-Variance Spanning Set


# Testing for the Minimum Mean-Variance Spanning Set









This paper explores the estimation and inference of the minimum spanning set (MSS), the smallest subset of risky assets that spans the mean-variance efficient frontier of the full asset set. We establish identification conditions for the MSS and develop a novel procedure for its estimation and inference. Our theoretical analysis shows that the proposed MSS estimator covers the true MSS with probability approaching 1 and converges asymptotically to the true MSS at any desired confidence level, such as 0.95 or 0.99. Monte Carlo simulations confirm the strong finite-sample performance of the MSS estimator. We apply our method to evaluate the relative importance of individual stock momentum and factor momentum strategies, along with a set of well-established stock return factors. The empirical results highlight factor momentum, along with several stock momentum and return factors, as key drivers of mean-variance efficiency. Furthermore, our analysis uncovers the sources of contribution from these factors and provides a ranking of their relative importance, offering new insights into their roles in mean-variance analysis.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。