IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1910.01781.html
   My bibliography  Save this paper

Distributionally Robust XVA via Wasserstein Distance: Wrong Way Counterparty Credit and Funding Risk

Author

Listed:
  • Derek Singh
  • Shuzhong Zhang

Abstract

This paper investigates calculations of robust XVA, in particular, credit valuation adjustment (CVA) and funding valuation adjustment (FVA) for over-the-counter derivatives under distributional uncertainty using Wasserstein distance as the ambiguity measure. Wrong way counterparty credit risk and funding risk can be characterized (and indeed quantified) via the robust XVA formulations. The simpler dual formulations are derived using recent infinite dimensional Lagrangian duality results. Next, some computational experiments are conducted to measure the additional XVA charges due to distributional uncertainty under a variety of portfolio and market configurations. Finally some suggestions for future work are discussed.

Suggested Citation

  • Derek Singh & Shuzhong Zhang, 2019. "Distributionally Robust XVA via Wasserstein Distance: Wrong Way Counterparty Credit and Funding Risk," Papers 1910.01781, arXiv.org, revised May 2020.
    • Handle: RePEc:arx:papers:1910.01781
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1910.01781
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Omar El Hajjaji & Alexander Subbotin, 2015. "Cva With Wrong Way Risk: Sensitivities, Volatility And Hedging," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 1-31.
    2. Paul Glasserman & Linan Yang, 2015. "Bounding Wrong-Way Risk in Measuring Counterparty Risk," Working Papers 15-16, Office of Financial Research, US Department of the Treasury.
    3. Jose Blanchet & Karthyek Murthy, 2019. "Quantifying Distributional Model Risk via Optimal Transport," Mathematics of Operations Research, INFORMS, vol. 44(2), pages 565-600, 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. Derek Singh & Shuzhong Zhang, 2019. "Distributionally Robust XVA via Wasserstein Distance Part 2: Wrong Way Funding Risk," Papers 1910.03993, arXiv.org.
    2. T. van der Zwaard & L. A. Grzelak & C. W. Oosterlee, 2022. "Efficient Wrong-Way Risk Modelling for Funding Valuation Adjustments," Papers 2209.12222, arXiv.org, revised Jun 2024.

    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. Derek Singh & Shuzhong Zhang, 2019. "Distributionally Robust XVA via Wasserstein Distance Part 2: Wrong Way Funding Risk," Papers 1910.03993, arXiv.org.
    2. Derek Singh & Shuzhong Zhang, 2020. "Distributionally Robust XVA via Wasserstein Distance: Wrong Way Counterparty Credit and Funding Risk," Applied Economics and Finance, Redfame publishing, vol. 7(6), pages 70-100, December.
    3. Viet Anh Nguyen & Fan Zhang & Shanshan Wang & Jose Blanchet & Erick Delage & Yinyu Ye, 2021. "Robustifying Conditional Portfolio Decisions via Optimal Transport," Papers 2103.16451, arXiv.org, revised Apr 2024.
    4. Meng Qi & Ying Cao & Zuo-Jun (Max) Shen, 2022. "Distributionally Robust Conditional Quantile Prediction with Fixed Design," Management Science, INFORMS, vol. 68(3), pages 1639-1658, March.
    5. Anthony Coache & Sebastian Jaimungal, 2024. "Robust Reinforcement Learning with Dynamic Distortion Risk Measures," Papers 2409.10096, arXiv.org.
    6. Kopa, Miloš & Rusý, Tomáš, 2023. "Robustness of stochastic programs with endogenous randomness via contamination," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1259-1272.
    7. 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.
    8. Kim, Sojung & Weber, Stefan, 2022. "Simulation methods for robust risk assessment and the distorted mix approach," European Journal of Operational Research, Elsevier, vol. 298(1), pages 380-398.
    9. Wu, Zhongqi & Jiang, Hui & Zhou, Yangye & Li, Haoyan, 2024. "Enhancing emergency medical service location model for spatial accessibility and equity under random demand and travel time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    10. Jose Blanchet & Karthyek Murthy & Nian Si, 2022. "Confidence regions in Wasserstein distributionally robust estimation [Distributionally robust groupwise regularization estimator]," Biometrika, Biometrika Trust, vol. 109(2), pages 295-315.
    11. Derek Singh & Shuzhong Zhang, 2020. "Robust Arbitrage Conditions for Financial Markets," Papers 2004.09432, arXiv.org.
    12. Len Patrick Dominic M. Garces & Yang Shen, 2024. "Robust optimal investment and consumption strategies with portfolio constraints and stochastic environment," Papers 2407.02831, arXiv.org.
    13. Derek Singh & Shuzhong Zhang, 2020. "Distributionally Robust Profit Opportunities," Papers 2006.11279, arXiv.org.
    14. Silvana Pesenti & Sebastian Jaimungal, 2020. "Portfolio Optimisation within a Wasserstein Ball," Papers 2012.04500, arXiv.org, revised Jun 2022.
    15. Bingyan Han, 2022. "Distributionally robust risk evaluation with a causality constraint and structural information," Papers 2203.10571, arXiv.org, revised Aug 2024.
    16. Sojung Kim & Stefan Weber, 2020. "Simulation Methods for Robust Risk Assessment and the Distorted Mix Approach," Papers 2009.03653, arXiv.org, revised Jan 2022.
    17. Zhao, Yue & Chen, Zhi & Lim, Andrew & Zhang, Zhenzhen, 2022. "Vessel deployment with limited information: Distributionally robust chance constrained models," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 197-217.
    18. Ruodu Wang & Zhenyuan Zhang, 2022. "Simultaneous Optimal Transport," Papers 2201.03483, arXiv.org, revised May 2023.
    19. Haolin Ruan & Zhi Chen & Chin Pang Ho, 2023. "Adjustable Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1002-1023, September.
    20. Fomichov, Vladimir & González Cázares, Jorge & Ivanovs, Jevgenijs, 2021. "Implementable coupling of Lévy process and Brownian motion," Stochastic Processes and their Applications, Elsevier, vol. 142(C), pages 407-431.

    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:1910.01781. 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.