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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- De Giorgi, Enrico, 2005.
"Reward-risk portfolio selection and stochastic dominance,"
Journal of Banking & Finance,
Elsevier, vol. 29(4), pages 895-926, April.
- Enrico De Giorgi, . "Reward-Risk Portfolio Selection and Stochastic Dominance," IEW - Working Papers 121, Institute for Empirical Research in Economics - University of Zurich.
- Yuichi Kitamura & Michael Stutzer, 1997. "An Information-Theoretic Alternative to Generalized Method of Moments Estimation," Econometrica, Econometric Society, vol. 65(4), pages 861-874, July.
- Stutzer, Michael, 1996. " A Simple Nonparametric Approach to Derivative Security Valuation," Journal of Finance, American Finance Association, vol. 51(5), pages 1633-52, December.
- Broniatowski, Michel & Keziou, Amor, 2009. "Parametric estimation and tests through divergences and the duality technique," Journal of Multivariate Analysis, Elsevier, vol. 100(1), pages 16-36, January.
- F. Douglas Foster & Charles H. Whiteman, 1999. "An Application of Bayesian Option Pricing to the Soybean Market," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(3), pages 722-727.
- Hans Föllmer & Alexander Schied, 2002. "Convex measures of risk and trading constraints," Finance and Stochastics, Springer, vol. 6(4), pages 429-447.
- Robertson, John C & Tallman, Ellis W & Whiteman, Charles H, 2005.
"Forecasting Using Relative Entropy,"
Journal of Money, Credit and Banking,
Blackwell Publishing, vol. 37(3), pages 383-401, June.
- Fishburn, Peter C, 1977. "Mean-Risk Analysis with Risk Associated with Below-Target Returns," American Economic Review, American Economic Association, vol. 67(2), pages 116-26, March.
- Toma, Aida & Broniatowski, Michel, 2011. "Dual divergence estimators and tests: Robustness results," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 20-36, January.
- Morales, D. & Pardo, L. & Vajda, I., 1997. "Some New Statistics for Testing Hypotheses in Parametric Models, ," Journal of Multivariate Analysis, Elsevier, vol. 62(1), pages 137-168, July.
- Toma, Aida & Leoni-Aubin, Samuela, 2010. "Robust tests based on dual divergence estimators and saddlepoint approximations," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1143-1155, May.
- Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
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