Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/13316
Title: Statistical analysis on markowitz portfolio mean-variance principle
Authors: LIU HUIXIA
Keywords: Portfolio Selection, Random Matrix, Bootstrap Method
Issue Date: 16-Aug-2007
Source: 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.
URI: http://scholarbank.nus.edu.sg/handle/10635/13316
Appears in Collections:Ph.D Theses (Open)

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