Comparison of robust optimization models for portfolio optimization

Official URL: https://risc01.sabanciuniv.edu/record=b2486366 _(Table of contents)

Using optimization techniques in portfolio selection has attracted significant attention in financial decisions. However, one of the main challenging aspects faced in optimal portfolio selection is that the models are sensitive to the estimations of the uncertain parameters. In this thesis, we focus on the robust optimization problems to incorporate uncertain parameters into the standard portfolio problems. First, we provide an overview of well-known optimization models when risk measures considered are variance, Value-at-Risk, and Conditional Value-at-Risk. Then, we provide reformulations of the robust versions of these portfolio optimization problems as conic programs when the uncertainty sets involve polytopic, ellipsoidal, or budgeted uncertainty for either mean return vector or covariance matrix or both. Finally, we conduct a computational study on two real data sets to evaluate and compare the effectiveness of the robust optimization approaches