Multiportfolio Optimization with CVaR Risk Measure

The vast majority of studies in portfolio optimization problem are conducted under a single portfolio framework. In the financial industry, the trading of multiple portfolios is usually aggregated and optimized simultaneously. When multiple portfolios are managed together, unique issues such as market impact costs must be dealt with properly. Conditional Value-at-Risk (CVaR) is a coherent risk measure with the computationally friendly feature of convexity. In this thesis, we propose the novel combination of CVaR with multiportfolio optimization (MPO) problem. To the best of our knowledge, this is the first work to use CVaR to measure risks in MPO problem and investigate the impact of CVaR on MPO problem. This thesis uses mathematical programming approaches to model MPO problem with CVaR. Four MPO models are developed considering fairness. The models are solved by GAMS software. Numerical experiments are conducted and analysed. The comparisons with existing methods and sensitivity analysis are reported