DescriptionIn this study, an application of novel risk modeling and optimization techniques to daily portfolio management will be described. In the first part, I develop and compare specialized methods for scenario generation and scenario tree construction. The quality of multi-stage stochastic optimization models depends heavily on the quality of the underlying scenario model. First, multivariate GO-GARCH model is used to generate adequate number of scenarios. Then, five different methods, a multi-facility location based backward scenario tree generation method, and forward and backward modified K-Means and Two-Step Cluster methods are used to generate scenario trees. Next, these five methods are tested on two-stage portfolio problems with different number of scenario sets. Finally, a Monge-Kantorovich transportation model is developed to compare the probability distribution of the GARCH-generated scenarios with the probability distribution in the constructed scenario trees. In the second part, I construct a two-stage stochastic programming problem with conditional measures of risk, which is used to re-balance the portfolio on a rolling horizon basis, with transaction costs included in the model. A conditional risk mapping approach will be used in the model so that information from the previous investment period can be used in the decision for the next investment period. Artzner et al. introduced coherent risk measures that reflect the interests of risk-averse investors. I will use coherent risk measures, such as semideviation risk function of order two or higher in this study. Next, the risk-averse multicut method, which is an extension of Bender’s decomposition and proposed originally for first-order risk measure by Miller and Ruszczy´nski, will be generalized to higher order risk measures in order to solve two-stage mean-risk portfolio problem. Performance of this method with the stated risk functions are evaluated on the scenario tree which is constructed in the first part. In the third part, I present an extensive simulation study on daily returns of Dow Jones companies by using several versions of the methodology. We show that two-stage models outperform single-stage models in terms of long-term performance. We also show that using high-order risk measures are superior to first-order measures.