Solving real-life portfolio problem using stochastic programming and Monte-Carlo techniques

Abstract

We deal with real-life portfolio problem with Value at Risk, transaction costs and integer allocations where the random returns are modeled using the multivariate skewed t-distribution. The ability to generate a feasible solution of the original chance constrained problem using the sample approximations of the chance constraints directly or via sample approximation of the penalty function objective is compared.

Keywords

• Value at Risk
• stochastic programming
• Monte-Carlo simulation
• chance constraints
• penalty functions