IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2001.06895.html
   My bibliography  Save this paper

Markov risk mappings and risk-sensitive optimal prediction

Author

Listed:
  • Tomasz Kosmala
  • Randall Martyr
  • John Moriarty

Abstract

We formulate a probabilistic Markov property in discrete time under a dynamic risk framework with minimal assumptions. This is useful for recursive solutions to risk-sensitive versions of dynamic optimisation problems such as optimal prediction, where at each stage the recursion depends on the whole future. The property holds for standard measures of risk used in practice, and is formulated in several equivalent versions including a representation via acceptance sets, a strong version, and a dual representation.

Suggested Citation

  • Tomasz Kosmala & Randall Martyr & John Moriarty, 2020. "Markov risk mappings and risk-sensitive optimal prediction," Papers 2001.06895, arXiv.org, revised Sep 2022.
    • Handle: RePEc:arx:papers:2001.06895
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2001.06895
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Saghafian, Soroush, 2018. "Ambiguous partially observable Markov decision processes: Structural results and applications," Journal of Economic Theory, Elsevier, vol. 178(C), pages 1-35.
    2. Sina Tutsch, 2008. "Update rules for convex risk measures," Quantitative Finance, Taylor & Francis Journals, vol. 8(8), pages 833-843.
    3. Nicole Bäuerle & Ulrich Rieder, 2014. "More Risk-Sensitive Markov Decision Processes," Mathematics of Operations Research, INFORMS, vol. 39(1), pages 105-120, February.
    4. Bartl, Daniel, 2020. "Conditional nonlinear expectations," Stochastic Processes and their Applications, Elsevier, vol. 130(2), pages 785-805.
    5. Erhan Bayraktar & Jingjie Zhang & Zhou Zhou, 2018. "Time Consistent Stopping For The Mean-Standard Deviation Problem --- The Discrete Time Case," Papers 1802.08358, arXiv.org, revised Apr 2019.
    6. Darinka Dentcheva & Spiridon Penev & Andrzej Ruszczyński, 2017. "Statistical estimation of composite risk functionals and risk optimization problems," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(4), pages 737-760, August.
    7. Marcel Nutz & Yuchong Zhang, 2019. "Conditional Optimal Stopping: A Time-Inconsistent Optimization," Papers 1901.05802, arXiv.org, revised Oct 2019.
    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. Soren Christensen & Kristoffer Lindensjo, 2019. "Time-inconsistent stopping, myopic adjustment & equilibrium stability: with a mean-variance application," Papers 1909.11921, arXiv.org, revised Jan 2020.
    2. Tomasz Kosmala & Randall Martyr & John Moriarty, 2023. "Markov risk mappings and risk-sensitive optimal prediction," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 97(1), pages 91-116, February.
    3. Qingda Wei & Xian Chen, 2023. "Continuous-Time Markov Decision Processes Under the Risk-Sensitive First Passage Discounted Cost Criterion," Journal of Optimization Theory and Applications, Springer, vol. 197(1), pages 309-333, April.
    4. O. L. V. Costa & F. Dufour, 2021. "Integro-differential optimality equations for the risk-sensitive control of piecewise deterministic Markov processes," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 93(2), pages 327-357, April.
    5. Frick, Mira & Iijima, Ryota & Le Yaouanq, Yves, 2022. "Objective rationality foundations for (dynamic) α-MEU," Journal of Economic Theory, Elsevier, vol. 200(C).
    6. Bhabak, Arnab & Saha, Subhamay, 2022. "Risk-sensitive semi-Markov decision problems with discounted cost and general utilities," Statistics & Probability Letters, Elsevier, vol. 184(C).
    7. Zhou, Zhou & Jin, Zhuo, 2020. "Optimal equilibrium barrier strategies for time-inconsistent dividend problems in discrete time," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 100-108.
    8. Föllmer Hans, 2014. "Spatial risk measures and their local specification: The locally law-invariant case," Statistics & Risk Modeling, De Gruyter, vol. 31(1), pages 1-23, March.
    9. Acciaio, Beatrice & Föllmer, Hans & Penner, Irina, 2012. "Risk assessment for uncertain cash flows: model ambiguity, discounting ambiguity, and the role of bubbles," LSE Research Online Documents on Economics 50118, London School of Economics and Political Science, LSE Library.
    10. Tomasz R. Bielecki & Igor Cialenco & Andrzej Ruszczy'nski, 2022. "Risk Filtering and Risk-Averse Control of Markovian Systems Subject to Model Uncertainty," Papers 2206.09235, arXiv.org.
    11. Aloisio Araujo & Alain Chateauneuf & José Heleno Faro & Bruno Holanda, 2019. "Updating pricing rules," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 68(2), pages 335-361, September.
    12. Bäuerle, Nicole & Rieder, Ulrich, 2017. "Zero-sum risk-sensitive stochastic games," Stochastic Processes and their Applications, Elsevier, vol. 127(2), pages 622-642.
    13. Constantin Waubert de Puiseau & Richard Meyes & Tobias Meisen, 2022. "On reliability of reinforcement learning based production scheduling systems: a comparative survey," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 911-927, April.
    14. Carlos Camilo-Garay & Rolando Cavazos-Cadena & Hugo Cruz-Suárez, 2022. "Contractive Approximations in Risk-Sensitive Average Semi-Markov Decision Chains on a Finite State Space," Journal of Optimization Theory and Applications, Springer, vol. 192(1), pages 271-291, January.
    15. Bäuerle, Nicole & Jaśkiewicz, Anna, 2017. "Optimal dividend payout model with risk sensitive preferences," Insurance: Mathematics and Economics, Elsevier, vol. 73(C), pages 82-93.
    16. Mira Frick & Ryota Iijima & Yves Le Yaouanq, 2020. "Objective rationality foundations for (dynamic) alpha-MEU," Cowles Foundation Discussion Papers 2244R, Cowles Foundation for Research in Economics, Yale University, revised Jul 2021.
    17. Yin Liu & Sam Davanloo Tajbakhsh, 2023. "Stochastic Composition Optimization of Functions Without Lipschitz Continuous Gradient," Journal of Optimization Theory and Applications, Springer, vol. 198(1), pages 239-289, July.
    18. Andrew J. Keith & Darryl K. Ahner, 2021. "A survey of decision making and optimization under uncertainty," Annals of Operations Research, Springer, vol. 300(2), pages 319-353, May.
    19. Adam Jonsson, 2023. "An axiomatic approach to Markov decision processes," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 97(1), pages 117-133, February.
    20. Berend Roorda & Johannes Schumacher, 2016. "Weakly time consistent concave valuations and their dual representations," Finance and Stochastics, Springer, vol. 20(1), pages 123-151, January.

    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:2001.06895. 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.