Please use this identifier to cite or link to this item:
Title: Statistical analysis on markowitz portfolio mean-variance principle
Keywords: Portfolio Selection, Random Matrix, Bootstrap Method
Issue Date: 16-Aug-2007
Citation: LIU HUIXIA (2007-08-16). Statistical analysis on markowitz portfolio mean-variance principle. ScholarBank@NUS Repository.
Abstract: The Markowitz mean-variance optimization procedure is highly appreciated as atheoretical result in literature. However, it has been demonstrated to be less applicable in practice. In this thesis, applying large dimensional data analysis, we first theoreticallyexplain that this phenomenon is natural when the number of asset is large. Inaddition, we theoretically prove that the estimated optimal return is always largerthan the theoretical value when the number of assets is large. To circumvent thisproblem, we employ large dimensional random matrix theory again to develop abootstrap method to correct the overprediction and reduce the estimation error.Our simulation results show that the bootsrap correction method can significantlyimprove the accuracy of the estimation. Furthermore, we investigate the asymptotic normality property of our bootstrapcorrected estimator. This will be useful in performing the hypothesis testing for thetheoretical return by using our bootstrap corrected estimator.
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
liuhuixia.pdf435.4 kBAdobe PDF



Page view(s)

checked on Apr 19, 2019


checked on Apr 19, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.