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Neural Network for CVA: Learning Future Values

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  • Jian-Huang She
  • Dan Grecu

Abstract

A new challenge to quantitative finance after the recent financial crisis is the study of credit valuation adjustment (CVA), which requires modeling of the future values of a portfolio. In this paper, following recent work in [Weinan E(2017), Han(2017)], we apply deep learning to attack this problem. The future values are parameterized by neural networks, and the parameters are then determined through optimization. Two concrete products are studied: Bermudan swaption and Mark-to-Market cross-currency swap. We obtain their expected positive/negative exposures, and further study the resulting functional form of future values. Such an approach represents a new framework for modeling XVA, and it also sheds new lights on other methods like American Monte Carlo.

Suggested Citation

  • Jian-Huang She & Dan Grecu, 2018. "Neural Network for CVA: Learning Future Values," Papers 1811.08726, arXiv.org.
    • Handle: RePEc:arx:papers:1811.08726
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    References listed on IDEAS

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    1. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    2. Yann LeCun & Yoshua Bengio & Geoffrey Hinton, 2015. "Deep learning," Nature, Nature, vol. 521(7553), pages 436-444, May.
    3. N. El Karoui & S. Peng & M. C. Quenez, 1997. "Backward Stochastic Differential Equations in Finance," Mathematical Finance, Wiley Blackwell, vol. 7(1), pages 1-71, January.
    4. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
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    Cited by:

    1. Jeong Yu Han & Patrick Rebentrost, 2022. "Quantum advantage for multi-option portfolio pricing and valuation adjustments," Papers 2203.04924, arXiv.org.
    2. King, Elizabeth M. & Randolph, Hannah L. & Floro, Maria S. & Suh, Jooyeoun, 2021. "Demographic, health, and economic transitions and the future care burden," World Development, Elsevier, vol. 140(C).
    3. Ludovic Goudenege & Andrea Molent & Antonino Zanette, 2022. "Computing XVA for American basket derivatives by Machine Learning techniques," Papers 2209.06485, arXiv.org.

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