Portfolio optimization based on divergence measures
AbstractA 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 f-divergence measure from information theory is used. There are many advantages to using the f-divergence 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 f-portfolios reduces to the expected utility function.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 43332.
Date of creation: Nov 2012
Date of revision:
Portfolio weights modeling; Divergence measures; Dual divergence; Information theory; Minimax optimization problems;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
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