IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1501.01504.html
   My bibliography  Save this paper

Optimal investment under behavioural criteria in incomplete diffusion market models

Author

Listed:
  • Mikl'os R'asonyi
  • Jos'e Gregorio Rodr'iguez-Villarreal

Abstract

The most commonly accepted model for investors' preferences is expected utility theory. More recently, other theories have emerged and pose new challenges to mathematics. The present paper treats preferences of cumulative prospect theory (CPT), where an "S-shaped" utility function is considered (i.e. convex up to a certain point and concave from there on). Also, distorted probability measures are applied for calculating the utility of a given position with respect to a (possibly random) benchmark $G$. Such problems have heretofore been solved essentially for complete continuous-time market models only. In the present paper we make a step forward and consider incomplete models of a diffusion type where the return of the investment in consideration depends on some economic factors. Our main result asserts, under mild assumptions, the existence of an optimal strategy when the driving noise of the economic factors is independent of that of the investment and the rate of return is non-negative. We are also able to accommodate models of a specific type where the factor may have non-zero correlation with the investment.

Suggested Citation

  • Mikl'os R'asonyi & Jos'e Gregorio Rodr'iguez-Villarreal, 2015. "Optimal investment under behavioural criteria in incomplete diffusion market models," Papers 1501.01504, arXiv.org.
  • Handle: RePEc:arx:papers:1501.01504
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1501.01504
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Miklós Rásonyi & Andrea Rodrigues, 2013. "Optimal portfolio choice for a behavioural investor in continuous-time markets," Annals of Finance, Springer, vol. 9(2), pages 291-318, May.
    2. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    3. Arjan B. Berkelaar & Roy Kouwenberg & Thierry Post, 2004. "Optimal Portfolio Choice under Loss Aversion," The Review of Economics and Statistics, MIT Press, vol. 86(4), pages 973-987, November.
    4. Quiggin, John, 1982. "A theory of anticipated utility," Journal of Economic Behavior & Organization, Elsevier, vol. 3(4), pages 323-343, December.
    5. Hanqing Jin & Xun Yu Zhou, 2008. "Behavioral Portfolio Selection In Continuous Time," Mathematical Finance, Wiley Blackwell, vol. 18(3), pages 385-426, July.
    6. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    7. repec:dau:papers:123456789/2317 is not listed on IDEAS
    8. Miklos Rasonyi & Andrea M. Rodrigues, 2012. "Optimal Portfolio Choice for a Behavioural Investor in Continuous-Time Markets," Papers 1202.0628, arXiv.org, revised Apr 2013.
    9. repec:dau:papers:123456789/5727 is not listed on IDEAS
    10. Krylov, N. V. & Liptser, R., 2002. "On diffusion approximation with discontinuous coefficients," Stochastic Processes and their Applications, Elsevier, vol. 102(2), pages 235-264, December.
    11. Alexander Cherny & Dilip Madan, 2009. "New Measures for Performance Evaluation," The Review of Financial Studies, Society for Financial Studies, vol. 22(7), pages 2371-2406, July.
    Full references (including those not matched with items on IDEAS)

    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. Huy N. Chau & Mikl'os R'asonyi, 2016. "Skorohod's representation theorem and optimal strategies for markets with frictions," Papers 1606.07311, arXiv.org, revised Apr 2017.
    2. Miklos Rasonyi, 2014. "Optimal investment with bounded above utilities in discrete time markets," Papers 1409.2023, arXiv.org.
    3. Marcos Escobar-Anel & Michel Kschonnek & Rudi Zagst, 2022. "Portfolio optimization: not necessarily concave utility and constraints on wealth and allocation," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 95(1), pages 101-140, February.
    4. Jakusch, Sven Thorsten & Meyer, Steffen & Hackethal, Andreas, 2019. "Taming models of prospect theory in the wild? Estimation of Vlcek and Hens (2011)," SAFE Working Paper Series 146, Leibniz Institute for Financial Research SAFE, revised 2019.
    5. De Giorgi, Enrico G. & Legg, Shane, 2012. "Dynamic portfolio choice and asset pricing with narrow framing and probability weighting," Journal of Economic Dynamics and Control, Elsevier, vol. 36(7), pages 951-972.
    6. Mikl'os R'asonyi & Andrea Meireles Rodrigues, 2013. "Continuous-Time Portfolio Optimisation for a Behavioural Investor with Bounded Utility on Gains," Papers 1309.0362, arXiv.org, revised Mar 2014.
    7. Bin Zou, 2017. "Optimal Investment In Hedge Funds Under Loss Aversion," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(03), pages 1-32, May.
    8. Massimiliano Amarante & Mario Ghossoub & Edmund Phelps, 2012. "Contracting for Innovation under Knightian Uncertainty," Cahiers de recherche 18-2012, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    9. Jakusch, Sven Thorsten, 2017. "On the applicability of maximum likelihood methods: From experimental to financial data," SAFE Working Paper Series 148, Leibniz Institute for Financial Research SAFE, revised 2017.
    10. Rania HENTATI & Jean-Luc PRIGENT, 2010. "Structured Portfolio Analysis under SharpeOmega Ratio," EcoMod2010 259600073, EcoMod.
    11. Wang, Suxin & Rong, Ximin & Zhao, Hui, 2019. "Optimal investment and benefit payment strategy under loss aversion for target benefit pension plans," Applied Mathematics and Computation, Elsevier, vol. 346(C), pages 205-218.
    12. Servaas van Bilsen & Roger J. A. Laeven & Theo E. Nijman, 2020. "Consumption and Portfolio Choice Under Loss Aversion and Endogenous Updating of the Reference Level," Management Science, INFORMS, vol. 66(9), pages 3927-3955, September.
    13. Stephen G Dimmock & Roy Kouwenberg & Olivia S Mitchell & Kim Peijnenburg, 2021. "Household Portfolio Underdiversification and Probability Weighting: Evidence from the Field," The Review of Financial Studies, Society for Financial Studies, vol. 34(9), pages 4524-4563.
    14. David B. BROWN & Enrico G. DE GIORGI & Melvyn SIM, 2009. "A Satiscing Alternative to Prospect Theory," Swiss Finance Institute Research Paper Series 09-19, Swiss Finance Institute.
    15. Caporin, Massimiliano & Costola, Michele & Jannin, Gregory & Maillet, Bertrand, 2018. "“On the (Ab)use of Omega?”," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 11-33.
    16. Huy N. Chau & Miklos Rasonyi, 2019. "Behavioural investors in conic market models," Papers 1903.08156, arXiv.org.
    17. Guo, Jing & He, Xue Dong, 2017. "Equilibrium asset pricing with Epstein-Zin and loss-averse investors," Journal of Economic Dynamics and Control, Elsevier, vol. 76(C), pages 86-108.
    18. Curatola, Giuliano, 2016. "Optimal consumption and portfolio choice with loss aversion," SAFE Working Paper Series 130, Leibniz Institute for Financial Research SAFE.
    19. Armstrong, John & Brigo, Damiano, 2019. "Risk managing tail-risk seekers: VaR and expected shortfall vs S-shaped utility," Journal of Banking & Finance, Elsevier, vol. 101(C), pages 122-135.
    20. Valeri Zakamouline & Steen Koekebakker, 2009. "A Generalisation of the Mean†Variance Analysis," European Financial Management, European Financial Management Association, vol. 15(5), pages 934-970, November.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:arx:papers:1501.01504. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.