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An Accurate Solution for Credit Valuation Adjustment (CVA) and Wrong Way Risk

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  • Xiao, Tim
Abstract
This paper presents a Least Square Monte Carlo approach for accurately calculating credit value adjustment (CVA). In contrast to previous studies, the model relies on the probability distribution of a default time/jump rather than the default time itself, as the default time is usually inaccessible. As such, the model can achieve a high order of accuracy with a relatively easy implementation. We find that the valuation of a defaultable derivative is normally determined via backward induction when their payoffs could be positive or negative. Moreover, the model can naturally capture wrong or right way risk. https://osf.io/preprints/socarxiv/3yjk2/download

Suggested Citation

  • Xiao, Tim, 2015. "An Accurate Solution for Credit Valuation Adjustment (CVA) and Wrong Way Risk," SocArXiv 3yjk2, Center for Open Science.
    • Handle: RePEc:osf:socarx:3yjk2
    DOI: 10.31219/osf.io/3yjk2
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    File URL: https://osf.io/download/5d82b442b3103d00194e8f8f/
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    Cited by:

    1. Xiao, Tim, 2018. "The Valuation of Financial Derivatives Subject to Counterparty Risk and Credit Value Adjustment," FrenXiv ds7zj, Center for Open Science.
    2. Tim Xiao, 2017. "A New Model for Pricing Collateralized Financial Derivatives," Post-Print hal-01800559, HAL.
    3. Xiao, Tim, 2018. "The Valuation of Credit Default Swap with Counterparty Risk and Collateralization," EconStor Preprints 203447, ZBW - Leibniz Information Centre for Economics.
    4. Pascal François & Weiyu Jiang, 2019. "Credit Value Adjustment with Market-implied Recovery," Journal of Financial Services Research, Springer;Western Finance Association, vol. 56(2), pages 145-166, October.
    5. David Lee, 2018. "Pricing Financial Derivatives Subject to Counterparty Risk and Credit Value Adjustment," Working Papers hal-01758922, HAL.

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