IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v7y2019i1p32-d212642.html
   My bibliography  Save this article

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

Author

Listed:
  • Zhuo Jin

    (Centre for Actuarial Studies, Department of Economics, The University of Melbourne, Melbourne, VIC 3010, Australia)

  • Zhixin Yang

    (Department of Mathematical Sciences, Ball State University, Muncie, IN 47306, USA)

  • Quan Yuan

    (Department of Mathematical Sciences, Ball State University, Muncie, IN 47306, USA)

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.

Suggested Citation

  • Zhuo Jin & Zhixin Yang & Quan Yuan, 2019. "A Genetic Algorithm for Investment–Consumption Optimization with Value-at-Risk Constraint and Information-Processing Cost," Risks, MDPI, vol. 7(1), pages 1-15, March.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:1:p:32-:d:212642
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/7/1/32/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/7/1/32/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Abel, Andrew B, 1990. "Asset Prices under Habit Formation and Catching Up with the Joneses," American Economic Review, American Economic Association, vol. 80(2), pages 38-42, May.
    2. Yiu, K. F. C., 2004. "Optimal portfolios under a value-at-risk constraint," Journal of Economic Dynamics and Control, Elsevier, vol. 28(7), pages 1317-1334, April.
    3. Jakub Steiner & Colin Stewart & Filip Matějka, 2017. "Rational Inattention Dynamics: Inertia and Delay in Decision‐Making," Econometrica, Econometric Society, vol. 85, pages 521-553, March.
    4. Michael J. Brennan & Yihong Xia, 2002. "Dynamic Asset Allocation under Inflation," Journal of Finance, American Finance Association, vol. 57(3), pages 1201-1238, June.
    5. Andrew B. Abel & Janice C. Eberly & Stavros Panageas, 2013. "Optimal Inattention to the Stock Market With Information Costs and Transactions Costs," Econometrica, Econometric Society, vol. 81(4), pages 1455-1481, July.
    6. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    7. Zhang, Nan & Jin, Zhuo & Li, Shuanming & Chen, Ping, 2016. "Optimal reinsurance under dynamic VaR constraint," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 232-243.
    8. Lixin Huang & Hong Liu, 2007. "Rational Inattention and Portfolio Selection," Journal of Finance, American Finance Association, vol. 62(4), pages 1999-2040, August.
    9. Constantinides, George M, 1990. "Habit Formation: A Resolution of the Equity Premium Puzzle," Journal of Political Economy, University of Chicago Press, vol. 98(3), pages 519-543, June.
    10. Chen, Shumin & Li, Zhongfei & Li, Kemian, 2010. "Optimal investment-reinsurance policy for an insurance company with VaR constraint," Insurance: Mathematics and Economics, Elsevier, vol. 47(2), pages 144-153, October.
    11. Christopher A. Sims, 2006. "Rational Inattention: Beyond the Linear-Quadratic Case," American Economic Review, American Economic Association, vol. 96(2), pages 158-163, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marc Chataigner & Stéphane Crépey, 2019. "Credit Valuation Adjustment Compression by Genetic Optimization," Risks, MDPI, vol. 7(4), pages 1-21, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christopher D. Carroll & Edmund Crawley & Jiri Slacalek & Kiichi Tokuoka & Matthew N. White, 2020. "Sticky Expectations and Consumption Dynamics," American Economic Journal: Macroeconomics, American Economic Association, vol. 12(3), pages 40-76, July.
    2. Luo, Yulei & Young, Eric, 2013. "Rational Inattention in Macroeconomics: A Survey," MPRA Paper 54267, University Library of Munich, Germany.
    3. Angeletos, G.-M. & Lian, C., 2016. "Incomplete Information in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 1065-1240, Elsevier.
    4. Yulei Luo & Eric R. Young, 2016. "Long‐Run Consumption Risk and Asset Allocation under Recursive Utility and Rational Inattention," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(2-3), pages 325-362, March.
    5. Pedro Bordalo & Nicola Gennaioli & Rafael La Porta & Andrei Shleifer, 2024. "Belief Overreaction and Stock Market Puzzles," Journal of Political Economy, University of Chicago Press, vol. 132(5), pages 1450-1484.
    6. Zhang, Yuhua & Mu, Congming, 2021. "Optimal ownership of entrepreneurial firms with rational inattention," Economics Letters, Elsevier, vol. 209(C).
    7. Atahan Afsar; José Elías Gallegos; Richard Jaimes; Edgar Silgado Gómez & José Elías Gallegos & Richard Jaimes & Edgar Silgado Gómez, 2020. "Reconciling Empirics and Theory: The Behavioral Hybrid New Keynesian Model," Vniversitas Económica 18560, Universidad Javeriana - Bogotá.
    8. Lindbeck, Assar & Weibull, Jörgen, 2020. "Delegation of investment decisions, and optimal remuneration of agents," European Economic Review, Elsevier, vol. 129(C).
    9. Bartosz Maćkowiak & Filip Matějka & Mirko Wiederholt, 2023. "Rational Inattention: A Review," Journal of Economic Literature, American Economic Association, vol. 61(1), pages 226-273, March.
    10. Ellison, Martin & Macaulay, Alistair, 2021. "A rational inattention unemployment trap," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    11. Pasten, Ernesto & Schoenle, Raphael, 2016. "Rational inattention, multi-product firms and the neutrality of money," Journal of Monetary Economics, Elsevier, vol. 80(C), pages 1-16.
    12. Macaulay, Alistair, 2021. "The attention trap: Rational inattention, inequality, and fiscal policy," European Economic Review, Elsevier, vol. 135(C).
    13. Francisco Gomes & Michael Haliassos & Tarun Ramadorai, 2021. "Household Finance," Journal of Economic Literature, American Economic Association, vol. 59(3), pages 919-1000, September.
    14. Matysková, Ludmila & Rogers, Brian & Steiner, Jakub & Sun, Keh-Kuan, 2020. "Habits as adaptations: An experimental study," Games and Economic Behavior, Elsevier, vol. 122(C), pages 391-406.
    15. Roc Armenter & Michèle Müller-Itten & Zachary Stangebye, 2020. "Rational Inattention via Ignorance Equivalence," Working Papers 20-24, Federal Reserve Bank of Philadelphia.
    16. Cai, Wenwu & Lu, Jing, 2019. "Investors’ financial attention frequency and trading activity," Pacific-Basin Finance Journal, Elsevier, vol. 58(C).
    17. Dewan, Ambuj & Neligh, Nathaniel, 2020. "Estimating information cost functions in models of rational inattention," Journal of Economic Theory, Elsevier, vol. 187(C).
    18. Martin, Daniel, 2017. "Strategic pricing with rational inattention to quality," Games and Economic Behavior, Elsevier, vol. 104(C), pages 131-145.
    19. Driscoll, John C. & Holden, Steinar, 2014. "Behavioral economics and macroeconomic models," Journal of Macroeconomics, Elsevier, vol. 41(C), pages 133-147.
    20. Dedola, Luca & Gautier, Erwan & Nakov, Anton & Santoro, Sergio & De Veirman, Emmanuel & Henkel, Lukas & Fagandini, Bruno, 2023. "Some implications of micro price-setting evidence for inflation dynamics and monetary transmission," Occasional Paper Series 321, European Central Bank.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jrisks:v:7:y:2019:i:1:p:32-:d:212642. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.