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Default Forecasting and Credit Valuation Adjustment

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Listed:
  • Lee, David

Abstract

Credit valuation adjustment has acquired a great deal of attention from both theoreticians and practitioners in recent years. This paper presents a model for default forecasting and credit valuation adjustment. The model links distance-to-default, default probability, survival probability, default correlation, and risky valuation together. It captures default risk, credit migration, and wrong way risk simultaneously and naturally. The numerical study shows that the model implied credit spreads and default correlations are very close to the market observed ones, indicating that the model performs quite well. The results may be of interest to regulators, academics, and practitioners.

Suggested Citation

  • Lee, David, 2023. "Default Forecasting and Credit Valuation Adjustment," MPRA Paper 118578, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:118578
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    References listed on IDEAS

    as
    1. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
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    4. Lijun Bo & Agostino Capponi, 2014. "Bilateral credit valuation adjustment for large credit derivatives portfolios," Finance and Stochastics, Springer, vol. 18(2), pages 431-482, April.
    5. Lokman Abbas-Turki & St'ephane Cr'epey & Bouazza Saadeddine, 2022. "Pathwise CVA Regressions With Oversimulated Defaults," Papers 2211.17005, arXiv.org.
    6. Mr. Jorge A Chan-Lau, 2006. "Market-Based Estimation of Default Probabilities and its Application to Financial Market Surveillance," IMF Working Papers 2006/104, International Monetary Fund.
    7. Paolo Barucca & Marco Bardoscia & Fabio Caccioli & Marco D'Errico & Gabriele Visentin & Guido Caldarelli & Stefano Battiston, 2020. "Network valuation in financial systems," Mathematical Finance, Wiley Blackwell, vol. 30(4), pages 1181-1204, October.
    8. Damiano Brigo & Fr'ed'eric Vrins, 2016. "Disentangling wrong-way risk: pricing CVA via change of measures and drift adjustment," Papers 1611.02877, arXiv.org.
    9. Stefan Nagel & Amiyatosh Purnanandam, 2020. "Banks’ Risk Dynamics and Distance to Default," The Review of Financial Studies, Society for Financial Studies, vol. 33(6), pages 2421-2467.
    10. Sreedhar T. Bharath & Tyler Shumway, 2008. "Forecasting Default with the Merton Distance to Default Model," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1339-1369, May.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

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