Portfolio optimization based on divergence measures
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 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.
|Date of creation:||Nov 2012|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://mpra.ub.uni-muenchen.de
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Toma, Aida & Broniatowski, Michel, 2011. "Dual divergence estimators and tests: Robustness results," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 20-36, January.
- 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.
- 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.
- 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.
- Hans Föllmer & Alexander Schied, 2002. "Convex measures of risk and trading constraints," Finance and Stochastics, Springer, vol. 6(4), pages 429-447.
- 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.
- John C. Robertson & Ellis W. Tallman & Charles H. Whiteman, 2002.
"Forecasting using relative entropy,"
2002-22, Federal Reserve Bank of Atlanta.
- 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.
- 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.
- Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
- Stutzer, Michael, 1996. " A Simple Nonparametric Approach to Derivative Security Valuation," Journal of Finance, American Finance Association, vol. 51(5), pages 1633-52, December.
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:43332. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ekkehart Schlicht)
If references are entirely missing, you can add them using this form.