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Risks 2019, 7(1), 32;

A Genetic Algorithm for Investment–Consumption Optimization with Value-at-Risk Constraint and Information-Processing Cost

Centre for Actuarial Studies, Department of Economics, The University of Melbourne, Melbourne, VIC 3010, Australia
Department of Mathematical Sciences, Ball State University, Muncie, IN 47306, USA
Author to whom correspondence should be addressed.
Received: 15 February 2019 / Revised: 8 March 2019 / Accepted: 9 March 2019 / Published: 11 March 2019
(This article belongs to the Special Issue Loss Models: From Theory to Applications)
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This paper studies 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. View Full-Text
Keywords: genetic algorithm; investment; Value-at-Risk; rational inattention genetic algorithm; investment; Value-at-Risk; rational inattention

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Jin, Z.; Yang, Z.; Yuan, Q. A Genetic Algorithm for Investment–Consumption Optimization with Value-at-Risk Constraint and Information-Processing Cost. Risks 2019, 7, 32.

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