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Limits of random walks with distributionally robust transition probabilities

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  • Daniel Bartl
  • Stephan Eckstein
  • Michael Kupper

Abstract

We consider a nonlinear random walk which, in each time step, is free to choose its own transition probability within a neighborhood (w.r.t. Wasserstein distance) of the transition probability of a fixed L\'evy process. In analogy to the classical framework we show that, when passing from discrete to continuous time via a scaling limit, this nonlinear random walk gives rise to a nonlinear semigroup. We explicitly compute the generator of this semigroup and corresponding PDE as a perturbation of the generator of the initial L\'evy process.

Suggested Citation

  • Daniel Bartl & Stephan Eckstein & Michael Kupper, 2020. "Limits of random walks with distributionally robust transition probabilities," Papers 2007.08815, arXiv.org, revised Apr 2021.
    • Handle: RePEc:arx:papers:2007.08815
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    References listed on IDEAS

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    1. Yu Feng & Ralph Rudd & Christopher Baker & Qaphela Mashalaba & Melusi Mavuso & Erik Schlögl, 2021. "Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models," Risks, MDPI, vol. 9(1), pages 1-20, January.
    2. Dolinsky, Yan & Nutz, Marcel & Soner, H. Mete, 2012. "Weak approximation of G-expectations," Stochastic Processes and their Applications, Elsevier, vol. 122(2), pages 664-675.
    3. Denk, Robert & Kupper, Michael & Nendel, Max, 2020. "A semigroup approach to nonlinear Lévy processes," Stochastic Processes and their Applications, Elsevier, vol. 130(3), pages 1616-1642.
    4. Wolfram Wiesemann & Daniel Kuhn & Berç Rustem, 2013. "Robust Markov Decision Processes," Mathematics of Operations Research, INFORMS, vol. 38(1), pages 153-183, February.
    5. Daniel Bartl & Samuel Drapeau & Ludovic Tangpi, 2020. "Computational aspects of robust optimized certainty equivalents and option pricing," Mathematical Finance, Wiley Blackwell, vol. 30(1), pages 287-309, January.
    6. Daniel Bartl, 2016. "Exponential utility maximization under model uncertainty for unbounded endowments," Papers 1610.00999, arXiv.org, revised Feb 2019.
    7. Jose Blanchet & Karthyek Murthy, 2019. "Quantifying Distributional Model Risk via Optimal Transport," Mathematics of Operations Research, INFORMS, vol. 44(2), pages 565-600, May.
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    Cited by:

    1. Blessing, Jonas & Kupper, Michael & Nendel, Max, 2023. "Convergence of Infintesimal Generators and Stability of Convex Montone Semigroups," Center for Mathematical Economics Working Papers 680, Center for Mathematical Economics, Bielefeld University.
    2. Max Nendel & Alessandro Sgarabottolo, 2022. "A parametric approach to the estimation of convex risk functionals based on Wasserstein distance," Papers 2210.14340, arXiv.org.

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