Improved Global Minimum Variance Portfolio via Tail Eigenvalues Amplification
报 告 人:: 翁成国
报告地点:: 数学与统计学院四楼报告厅
报告时间:: 2017年07月06日星期四10:00-11:00
报告简介:

We discuss how a manipulation of sample eigenvalues affects the actual portfolio risk when a two-step method is used to construct a global minimum variance portfolio (GMVP). For special structures of the true covariance matrix, both a shrinkage on a head eigenvalue and an amplification on a tail eigenvalue are shown to have a marginal effect of reducing the actual risk. In a high-dimensional setting, the marginal effect of amplifying a tail eigenvalue becomes dominant compared with that of shrinking a head eigenvalue. This leads us to propose a new concept called a tail eigenvalues amplification (TEA) method. In the TEA method, the first few eigenvalues are kept unchanged, while the last few eigenvalues are amplified to infinity. This modified covariance matrix is used for constructing a GMVP. Both simulation and empirical results show that the TEA method with the number of eigenvalues to amplify selected using a cross-validation method has an improved actual portfolio risk reduction effect and smaller turnover compared with the “shrinkage towards identity” method. Lastly, when there is a larger gap (in terms of the magnitude) between the off-diagonal elements and the diagonal elements in the true covariance matrix, the TEA method results in a higher percentage actual portfolio risk reduction.

举办单位:数学与统计学院
发 布 人:科研助理 发布时间: 2017-07-03
主讲人简介:
翁成国,本科硕士毕业于浙江大学,博士毕业于滑铁卢大学统计与精算系,博士生导师,现任加拿大滑铁卢大学统计与精算系副教授。翁成国博士在精算学、金融数学、随机优化、统计等领域发表论文30篇,其中多篇发表在国际著名精算或统计杂志,所有文章均被SCI或SSCI收录。获得加拿大国家自然和工程研究基金项目资助两次和北美精算学会项目两项。