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Distributionally Robust XVA via Wasserstein Distance Part 2: Wrong Way Funding Risk

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  • Derek Singh
  • Shuzhong Zhang

Abstract

This paper investigates calculations of robust funding valuation adjustment (FVA) for over the counter (OTC) derivatives under distributional uncertainty using Wasserstein distance as the ambiguity measure. Wrong way funding risk can be characterized via the robust FVA formulation. The simpler dual formulation of the robust FVA optimization is derived. Next, some computational experiments are conducted to measure the additional FVA charge due to distributional uncertainty under a variety of portfolio and market configurations. Finally some suggestions for future work, such as robust capital valuation adjustment (KVA) and margin valuation adjustment (MVA), are discussed.

Suggested Citation

  • Derek Singh & Shuzhong Zhang, 2019. "Distributionally Robust XVA via Wasserstein Distance Part 2: Wrong Way Funding Risk," Papers 1910.03993, arXiv.org.
    • Handle: RePEc:arx:papers:1910.03993
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    File URL: http://arxiv.org/pdf/1910.03993
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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. Derek Singh & Shuzhong Zhang, 2020. "Tight Bounds for a Class of Data-Driven Distributionally Robust Risk Measures," Papers 2010.05398, arXiv.org, revised Oct 2020.

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