发帖
雷达卡
英文标题：
《A Robust Statistics Approach to Minimum Variance Portfolio Optimization》
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作者：
Liusha Yang, Romain Couillet, Matthew R. McKay
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最新提交年份：
2015
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英文摘要：
We study the design of portfolios under a minimum risk criterion. The performance of the optimized portfolio relies on the accuracy of the estimated covariance matrix of the portfolio asset returns. For large portfolios, the number of available market returns is often of similar order to the number of assets, so that the sample covariance matrix performs poorly as a covariance estimator. Additionally, financial market data often contain outliers which, if not correctly handled, may further corrupt the covariance estimation. We address these shortcomings by studying the performance of a hybrid covariance matrix estimator based on Tyler\'s robust M-estimator and on Ledoit-Wolf\'s shrinkage estimator while assuming samples with heavy-tailed distribution. Employing recent results from random matrix theory, we develop a consistent estimator of (a scaled version of) the realized portfolio risk, which is minimized by optimizing online the shrinkage intensity. Our portfolio optimization method is shown via simulations to outperform existing methods both for synthetic and real market data.
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中文摘要：
我们研究了最小风险准则下的投资组合设计。优化组合的性能取决于组合资产收益的估计协方差矩阵的准确性。对于大型投资组合，可用市场回报的数量通常与资产的数量具有相似的顺序，因此样本协方差矩阵作为协方差估计器的性能较差。此外，金融市场数据通常包含异常值，如果处理不当，可能会进一步破坏协方差估计。我们通过研究基于泰勒稳健M-估计和Ledoit-Wolf收缩估计的混合协方差矩阵估计的性能来解决这些缺点，同时假设样本具有重尾分布。利用随机矩阵理论的最新结果，我们开发了一个已实现投资组合风险的一致性估计量（一个标度版本），通过在线优化收缩强度使其最小化。通过仿真，我们的投资组合优化方法在合成和真实市场数据方面都优于现有方法。
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分类信息：
一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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