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A Note on the Vasicek’s Model with the Logistic Distribution



The paper argues that it would be natural to replace the standard normal distribution function by the logistic function in the regulatory Basel II (Vasicek’s) formula. Such a model would be in fact consistent with the standard logistic regression PD modeling approach. An empirical study based on US commercial bank’s loan historical delinquency rates re-estimates the default correlations and unexpected losses for the normal and logistic distribution models. The results indicate that the capital requirements could be up to 100% higher if the normal Vasicek’s model was replaced by the logistic one.

Suggested Citation

  • Jiri Witzany, 2013. "A Note on the Vasicek’s Model with the Logistic Distribution," Working Papers IES 2013/01, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jan 2013.
  • Handle: RePEc:fau:wpaper:wp2013_01

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    References listed on IDEAS

    1. Jérôme Vandenbussche & Ursula Vogel & Enrica Detragiache, 2015. "Macroprudential Policies and Housing Prices: A New Database and Empirical Evidence for Central, Eastern, and Southeastern Europe," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(S1), pages 343-377, March.
    2. Michal Brzoza-Brzezina, 2005. "Lending Booms in Europe’s Periphery: South-Western Lessons for Central-Eastern Members," Macroeconomics 0502002, EconWPA.
    3. Christian Dembiermont & Mathias Drehmann & Siriporn Muksakunratana, 2013. "How much does the private sector really borrow - a new database for total credit to the private non-financial sector," BIS Quarterly Review, Bank for International Settlements, March.
    4. Peter Backé & Balázs Égert & Zoltan Walko, 2007. "Credit Growth in Central and Eastern Europe Revisited," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 69-77.
    5. Frait, Jan & Gersl, Adam & Seidler, Jakub, 2011. "Credit growth and financial stability in the Czech Republic," Policy Research Working Paper Series 5771, The World Bank.
    6. Jeffrey M. Wooldridge, 2003. "Cluster-Sample Methods in Applied Econometrics," American Economic Review, American Economic Association, vol. 93(2), pages 133-138, May.
    7. Kraft, Evan & Jankov, Ljubinko, 2005. "Does speed kill? Lending booms and their consequences in Croatia," Journal of Banking & Finance, Elsevier, vol. 29(1), pages 105-121, January.
    8. Giovanni Dell'Ariccia & Pau Rabanal & Christopher W. Crowe & Deniz O Igan, 2011. "Policies for Macrofinancial Stability; Options to Deal with Real Estate Booms," IMF Staff Discussion Notes 11/02, International Monetary Fund.
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    Cited by:

    1. Hana Džmuráňová & Petr Teplý, . "Why Are Savings Accounts Perceived as Risky Bank Products?," Prague Economic Papers, University of Economics, Prague, vol. 0, pages 1-17.
    2. Hana Džmuráňová & Petr Teplý, 2016. "Why Are Savings Accounts Perceived as Risky Bank Products?," Prague Economic Papers, University of Economics, Prague, vol. 2016(5), pages 617-633.

    More about this item


    credit risk; Basel II regulation; default rates;

    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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