报告题目: Stochastic Optimization Algorithms for Mean-Variance Management of a Defined Contribution Pension under Rational Inattention
报告人：Zhixin Yang (Assistant Professor, Ball State University)
报告时间：2019年7月20日（星期六），16:30—17:30
报告地点：龙赛理科楼，博彩导航
116
报告摘要: This paper studies the optimal investment strategies of a defined contribution pension plan. Con sidering the mortality risk and inflation risk, a pensioner manages his defined contribution plan under the mean-variance framework throughout the working life. The volatile economy states throughout the working life is described as a hidden Markov regime-switching process. The pensioner has to filter the key economic factors to make decisions. By using the Wonham filter, a partially observed system is converted to a completely observed one. However, due to the finite information-processing capability in the long term, the pensioner fail to process all of the information in a rational manner and can only make decisions based on the limited observed signals. A stochastic approximation algorithm is developed to find the optimal investment strategies and observation strength. Convergence of the algorithm and rate of convergence are presented. Numerical examples are provided to illustrate the performance of the numerical method.
报告题目: A Genetic Algorithm for Investment-Consumption Optimization with Value-at-Risk Constraint and Information-Processing Cost
报告人：Yuan chuan (Assistant Professor, Ball State University)
报告时间：2019年7月20日（星期六），17:30—18:30
报告地点：龙赛理科楼，博彩导航
116
报告摘要: This study focuses on the optimal investment and consumption strategies in a two-asset model. A dynamic Value-at-Risk constraint is imposed to manage the wealth process. By using Value at Risk as the risk measure during the investment horizon, the decision maker can dynamically monitor the exposed risk and quantify the maximum expected loss over a finite horizon period at a given confidence level. In addition, the decision maker has to filter the key economic factors to make decisions. Considering the cost of filtering the factors, the decision maker aims to maximize the utility of consumption in a finite horizon. By using the Kalman filter, a partially observed system is converted to a completely observed one. However, due to the cost of information processing, the decision maker fails to process the information in an arbitrarily rational manner and can only make decisions on the basis of the limited observed signals. A genetic algorithm was developed to find the optimal investment, consumption strategies, and observation strength. Numerical simulation results are provided to illustrate the performance of the algorithm.
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