Grant: Engage Grants Program

One of the most interesting and attractive areas of study that has the potential to provide significant benefit for (company) is in the decision-making process under conditions of uncertainty is financial optimization. Prominent examples include: 1) risk management for large public corporations; 2) hedge fund strategies to capitalize on market conditions; 3) security selection for stock and bond portfolio managers; 4) currency hedging for multi-national corporations; 5) asset allocation for pension plans and insurance companies. In thesesituations, time periods and uncertainties play important roles. For example, a pension plan manager must focus on both the long-term and short-term consequences of his/her investment strategy. One must attempt to minimize pension contribution expenses over time, while satisfying the needs of the retirees, and also reducing risks. There are also many uncertainties in financial planning, such as economic factors, prices of the securitiesconsidered, amount of cash flow, etc. Multi-stage stochastic programming models provide the best option to address these significant practical issues. Stochastic programming provides a general purpose-modeling framework, which can capture real-world features such as turnover constraints, transaction costs, risk aversion, limits on groups of assets and other considerations. In this project, we will be studying both static one-period applications and dynamic modeling over time to provide options for the enhancement of (the company's)operations. Previous researchers have shown that parameters are difficult to estimate and are time varying; however, the means has been shown to be the most important to accurately estimate. Hence, probability distributions will be based on models that estimate one-period and multi-period scenarios - one does not have to assume that the parameters in these models are known. We will model transaction costs, market impact costs, liquidity and other market imperfections as stochastic optimization with constraints. We will also consider portfolio management under uncertainty and scenario optimization stochastic programming-based, multi-period asset-liability models. Furthermore we will study static single period and dynamic multi-period