000196210 001__ 196210
000196210 005__ 20181203023419.0
000196210 037__ $$aARTICLE
000196210 245__ $$aDynamic Mean-Variance Portfolio Analysis under Model Risk
000196210 269__ $$a2009
000196210 260__ $$c2009
000196210 336__ $$aJournal Articles
000196210 520__ $$aThe classical Markowitz approach to portfolio selection is compromised by two major shortcomings. First, there is considerable model risk with respect to the distribution of asset returns. Particularly, mean returns are notoriously difficult to estimate. Moreover, the Markowitz approach is static in that it does not account for the possibility of portfolio rebalancing within the investment horizon. We propose a robust dynamic portfolio optimization model to overcome both shortcomings. The model arises from an infinite-dimensional min-max framework. The objective is to minimize the worst-case portfolio variance over a family of dynamic investment strategies subject to a return target constraint. The worst-case variance is evaluated with respect to a set of conceivable return distributions. We develop a quantitative approach to approximate this intractable problem by a tractable one and report on numerical experiments.
000196210 700__ $$0247589$$aKuhn, Daniel$$g239987
000196210 700__ $$aParpas, Panos
000196210 700__ $$aRustem, Berç
000196210 700__ $$aFonseca, Raquel
000196210 773__ $$j12$$k4$$q91-115$$tJournal of Computational Finance
000196210 8564_ $$uhttp://www.risk.net/journal-of-computational-finance/technical-paper/2160420/dynamic-mean-variance-portfolio-analysis-model-risk$$zURL
000196210 909C0 $$0252496$$pRAO$$xU12788
000196210 909CO $$ooai:infoscience.tind.io:196210$$pCDM$$particle
000196210 917Z8 $$x112541
000196210 937__ $$aEPFL-ARTICLE-196210
000196210 973__ $$aOTHER$$rNON-REVIEWED$$sPUBLISHED
000196210 980__ $$aARTICLE