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Bank Risk Dynamics and Distance to Default

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  • Stefan Nagel
  • Amiyatosh Purnanandam

Abstract

We adapt structural models of default risk to take into account the special nature of bank assets. The usual assumption of log-normally distributed asset values is not appropriate for banks. Typical bank assets are risky debt claims, which implies that they embed a short put option on the borrowers’ assets, leading to a concave payoff. This has important consequences for banks’ risk dynamics and distance to default estimation. Due to the payoff non-linearity, bank asset volatility rises following negative shocks to borrower asset values. As a result, standard structural models in which the asset volatility is assumed to be constant can severely understate banks’ default risk in good times when asset values are high. Bank equity payoffs resemble a mezzanine claim rather than a call option. Bank equity return volatility is therefore much more sensitive to big negative shocks to asset values than in standard structural models.

Suggested Citation

  • Stefan Nagel & Amiyatosh Purnanandam, 2019. "Bank Risk Dynamics and Distance to Default," NBER Working Papers 25807, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:25807
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    Cited by:

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    2. Carmelo Salleo & Alberto Grassi & Constantinos Kyriakopoulos, 2020. "A Comprehensive Approach for Calculating Banking Sector Risks," IJFS, MDPI, vol. 8(4), pages 1-21, November.
    3. Kuvshinov, Dmitry & Richter, Björn & Zimmermann, Kaspar, 2022. "The shifts and the shocks: bank risk, leverage, and the macroeconomy," Working Paper Series 2672, European Central Bank.
    4. Caterina Mendicino & Kalin Nikolov & Juan Rubio-Ramirez & Javier Suarez, 2020. "Twin Default Crises," Working Papers 2020-01, FEDEA.
    5. Erasmo Giambona & Rafael Matta & José-Luis Peydró & Ye Wang, 2020. "Quantitative easing, investment, and safe assets: the corporate-bond lending channel," Economics Working Papers 1722, Department of Economics and Business, Universitat Pompeu Fabra, revised Oct 2020.
    6. David Xiao, 2023. "Default Process Modeling and Credit Valuation Adjustment," Papers 2309.03311, arXiv.org.
    7. Itamar Drechsler & Alexi Savov & Philipp Schnabl, 2018. "Banking on Deposits: Maturity Transformation without Interest Rate Risk," NBER Working Papers 24582, National Bureau of Economic Research, Inc.
    8. Lee, David, 2023. "Default Forecasting and Credit Valuation Adjustment," MPRA Paper 118578, University Library of Munich, Germany.
    9. Lukasz Prorokowski, 2016. "Bankrupt UK cities: PD model for credit risk in sub-sovereign sector," Bank i Kredyt, Narodowy Bank Polski, vol. 47(6), pages 495-528.
    10. Peleg Lazar, Sharon & Raviv, Alon, 2019. "The risk spiral: The effects of bank capital and diversification on risk taking," International Review of Financial Analysis, Elsevier, vol. 65(C).
    11. Atreya, Nikhil & Mjøs, Aksel & Persson, Svein-Arne, 2015. "Making Bank: Why High Bank Leverage is Optimal - for the Bank's Shareholders," Discussion Papers 2015/33, Norwegian School of Economics, Department of Business and Management Science.
    12. Antonio Sánchez Serrano, 2018. "EU banks after the crisis: sinners in the hands of angry markets," Journal of Banking and Financial Economics, University of Warsaw, Faculty of Management, vol. 1(9), pages 24-51, May.
    13. Matthias Fleckenstein & Francis A. Longstaff, 2023. "Small Business Equity Returns: Empirical Evidence from the Business Credit Card Securitization Market," Journal of Finance, American Finance Association, vol. 78(1), pages 389-425, February.

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    More about this item

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation

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