Chalabi, Yohan and Wuertz, Diethelm (2012): Portfolio optimization based on divergence measures.

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Abstract
A new portfolio selection framework is introduced where the investor seeks the allocation that is as close as possible to his "ideal" portfolio. To build such a portfolio selection framework, the fdivergence measure from information theory is used. There are many advantages to using the fdivergence measure. First, the allocation is made such that it is in agreement with the historical data set. Second, the divergence measure is a convex function, which enables the use of fast optimization algorithms. Third, the objective value of the minimum portfolio divergence measure provides an indication distance from the ideal portfolio. A statistical test can therefore be constructed from the value of the objective function. Fourth, with adequate choices of both the target distribution and the divergence measure, the objective function of the fportfolios reduces to the expected utility function.
Item Type:  MPRA Paper 

Original Title:  Portfolio optimization based on divergence measures 
Language:  English 
Keywords:  Portfolio weights modeling; Divergence measures; Dual divergence; Information theory; Minimax optimization problems 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C43  Index Numbers and Aggregation G  Financial Economics > G1  General Financial Markets > G11  Portfolio Choice ; Investment Decisions C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C12  Hypothesis Testing: General C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61  Optimization Techniques ; Programming Models ; Dynamic Analysis 
Item ID:  43332 
Depositing User:  Yohan Chalabi 
Date Deposited:  20 Dec 2012 12:06 
Last Modified:  27 Sep 2019 18:01 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/43332 