A genetic algorithm for investment–consumption optimization with value-at-risk constraint and information-processing cost

Zhuo Jin*, Zhixin Yang, Quan Yuan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
22 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number32
Pages (from-to)1-15
Number of pages15
JournalRisks
Volume7
Issue number1
DOIs
Publication statusPublished - Mar 2019
Externally publishedYes

Bibliographical note

Copyright © 2019 by the authors. Licensee MDPI, Basel, Switzerland. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Genetic algorithm
  • Investment
  • Rational inattention
  • Value-at-Risk

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