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Modeling Recoveries of US Leading Banks Based on Publicly Disclosed Data

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  • Pawel Siarka

    (Wroclaw University of Economics and Business, 53-345 Wroclaw, Poland)

Abstract

The credit risk management process is a critical element that allows financial institutions to withstand economic downturns. Unlike the methods regarding the probability of default, which have been deeply addressed after the financial crisis in 2008, recovery rate models still need further development. As there are no industry standards, leading banks are modeling recovery rates using internal models developed with different assumptions. Therefore, the outcomes are often incomparable and may lead to confusion. The author presents the concept of a unified recovery rate analysis for US banks. He uses data derived from FR Y-9C reports disclosed by the Federal Reserve Bank of Chicago. Based on the historical recoveries and credit portfolio book values, the author examines the distribution function of recoveries. The research refers to a credit card portfolio and covers nine leading US banks. The author leveraged Vasicek’s one-factor model with the asset correlation parameter and implemented it for recovery rate analysis. This experiment revealed that the estimated latent correlation ranges from 0.2% to 1.5% within the examined portfolios. They are large enough to impact the recovery rate volatility and cannot be treated as negligible. It was shown that the presented method could be applied under US Comprehensive Capital Analysis and Review exercise.

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  • Pawel Siarka, 2021. "Modeling Recoveries of US Leading Banks Based on Publicly Disclosed Data," Mathematics, MDPI, vol. 9(2), pages 1-14, January.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:2:p:188-:d:482845
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    References listed on IDEAS

    as
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    2. Unal, Haluk & Madan, Dilip & Guntay, Levent, 2003. "Pricing the risk of recovery in default with absolute priority rule violation," Journal of Banking & Finance, Elsevier, vol. 27(6), pages 1001-1025, June.
    3. Düllmann, Klaus & Trapp, Monika, 2004. "Systematic Risk in Recovery Rates: An Empirical Analysis of US Corporate Credit Exposures," Discussion Paper Series 2: Banking and Financial Studies 2004,02, Deutsche Bundesbank.
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    6. Rongda Chen & Ze Wang, 2013. "Curve Fitting of the Corporate Recovery Rates: The Comparison of Beta Distribution Estimation and Kernel Density Estimation," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-9, July.
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