ESSAYS ON DISTRIBUTIONALLY ROBUST PORTFOLIO OPTIMIZATION Ousawat, Thitapon Interest in distributionally robust optimization has been increasing recently. In this dissertation, we review recent developments in the literature in this eld and propose a model for distributionally robust mean-risk portfolio optimization. The model optimizes a risk-averse objective function with the worst-case return as reward and worse-case conditional Value-at-Risk as the risk measure. The model considers ambiguity in the distribution of data used to estimate the asset returns in the optimization model by creating an ambiguity set using -divergence measures which measure the distance between vectors. A numerical example is shown using the Kullback-Leibler divergence measure as the -divergence measure. A model for distributionally robust portfolio optimization with transaction costs is used to compare the performance of a distributionally robust mean-CVaR portfolio with the nominal as well as equally-weighted portfolio. The result shows that, under certain conditions, the distributionally robust model performs better than both the nominal and equally-weighted portfolio. PH.D in Management Science, July 2013 Sabbaghi, Navid 2013 2013-07 Dissertation application/pdf islandora:6974 http://hdl.handle.net/10560/3182 SSB / Stuart School of Business Illinois Institute of Technology en In Copyright http://rightsstatements.org/page/InC/1.0/ Restricted Access