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Granularity adjustment for Basel II

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Author Info

  • Lütkebohmert, Eva
  • Gordy, Michael B.

Abstract

The credit value-at-risk model underpinning the Basel II Internal Ratings-Based approach assumes that idiosyncratic risk has been diversified away fully in the portfolio, so that economic capital depends only on systematic risk contributions. We develop a simple methodology for approximating the effect of undiversified idiosyncratic risk on VaR. The supervisory review process (Pillar 2) of the new Basel framework offers a potential venue for application of the proposed granularity adjustment (GA). Our GA is a revision and extension of the methodology proposed in the Basel II Second Consultative Paper. The revision incorporates some technical advances as well as modifications to the Basel II rules since the Second Consultative Paper of 2001. Most importantly, we introduce an ?upper bound? methodology under which banks would be required to aggregate multiple exposures to the same underlying obligor only for a subset of their obligors. This addresses what appears to be the most significant operational burden associated with any rigorous assessment of residual idiosyncratic risk in the portfolio. For many banks, this approach would permit dramatic reductions in data requirements relative to the full GA. -- Adressenkonzentration in einem Kreditportfolio entsteht, wenn sehr wenige Kreditnehmer in dem Portfolio sind oder wenn die Kreditvolumina sehr ungleich verteilt sind. Das Kreditrisikomodell, welches dem Internen Ratings-Basierten (IRB) Ansatz von Basel II unterliegt, berücksichtigt die Adressenkonzentration nicht. Es wird vielmehr sogar angenommen, dass das Portfolio einer Bank perfekt granular ist, in dem Sinne, dass jeder einzelne Kredit nur einen sehr kleinen Anteil zum Gesamtportfolio beiträgt. Reale Bankportfolios sind selbstverständlich nicht perfekt granular. In dieser Arbeit stellen wir eine einfache Granularitätsanpassung (GA) als Methode vor, mit der der Beitrag von Adressenkonzentration zum Risiko eines Portfolios quantifiziert werden kann. Das bankenaufsichtliche Überprüfungsverfahren (Säule 2) unter Basel II bietet ein Anwendungsfeld für die vorgeschlagene Methode. Diese Version der GA ist eine Überarbeitung und Erweiterung früherer Methoden und dient insbesondere dazu, die Kosten für eine Umsetzung in der Praxis zu reduzieren. In praktischen Anwendungen stellen meistens die benötigten Daten (und nicht die Formel, die auf diese Daten angewendet wird) das größte Hindernis dar. Wenn eine Bank mehrere Kredite an denselben Kreditnehmer vergeben hat, erfordert die Messung von Adressenkonzentration, dass diese Kredite aggregiert werden. Das ist unabhängig davon, ob die von uns vorgeschlagene Methode oder eine beliebige robuste Alternative verwendet wird. Diese Aggregation von Kreditinformationen stellt momentan eine wesentliche Herausforderung für die Banken dar. Unsere überarbeitete GA bietet den Banken die Möglichkeit, eine obere Schranke für die GA in einem Portfolio zu berechnen, indem sie sich ausschließlich auf Informationen über die größten Kredite stützt. Dadurch entfällt die Notwendigkeit, Daten für jeden einzelnen Kreditnehmer zu aggregieren. Für viele Banken würde dieser Ansatz eine erhebliche Reduktion der Datenanforderungen im Vergleich zu früheren Methoden zur Bestimmung der Granularitätsanpassung darstellen. Wir wenden unsere GA Methode auf mehrere realistische Portfolios an, die auf dem Millionenkreditmeldewesen basieren. Unsere Ergebnisse zeigen, dass der Effekt der Adressenkonzentration bedeutend sein kann und dass die von uns vorgeschlagene GA eine robuste Methode für ihre Messung darstellt.

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Bibliographic Info

Paper provided by Deutsche Bundesbank, Research Centre in its series Discussion Paper Series 2: Banking and Financial Studies with number 2007,01.

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Date of creation: 2007
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Handle: RePEc:zbw:bubdp2:5353

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Related research

Keywords: Basel II; granularity adjustment; value-at-risk; idiosyncratic risk;

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References

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  1. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Learning, Structural Instability and Present Value Calculations," CESifo Working Paper Series 1650, CESifo Group Munich.
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Cited by:
  1. Mager, Ferdinand & Schmieder, Christian, 2008. "Stress testing of real credit portfolios," Discussion Paper Series 2: Banking and Financial Studies 2008,17, Deutsche Bundesbank, Research Centre.
  2. Sanjiv Das, 2007. "Basel II: Correlation Related Issues," Journal of Financial Services Research, Springer, vol. 32(1), pages 17-38, October.
  3. Tarashev, Nikola, 2010. "Measuring portfolio credit risk correctly: Why parameter uncertainty matters," Journal of Banking & Finance, Elsevier, vol. 34(9), pages 2065-2076, September.
  4. Nikola A. Tarashev & Haibin Zhu, 2007. "Modelling and calibration errors in measures of portfolio credit risk," BIS Working Papers 230, Bank for International Settlements.
  5. Sebastian Ebert & Eva Lütkebohmert, 2009. "Improved Modeling of Double Default Effects in Basel II - An Endogenous Asset Drop Model without Additional Correlation," Bonn Econ Discussion Papers bgse24_2009, University of Bonn, Germany.
  6. Nikola Tarashev & Haibin Zhu, 2008. "Specification and Calibration Errors in Measures of Portfolio Credit Risk: The Case of the ASRF Model," International Journal of Central Banking, International Journal of Central Banking, vol. 4(2), pages 129-173, June.
  7. Klaus Duellmann & Martin Erdelmeier, 2009. "Crash Testing German Banks," International Journal of Central Banking, International Journal of Central Banking, vol. 5(3), pages 139-175, September.
  8. Gürtler, Marc & Hibbeln, Martin & Vöhringer, Clemens, 2007. "Measuring concentration risk for regulatory purposes," Working Papers IF26V4, Technische Universität Braunschweig, Institute of Finance.
  9. Gourieroux, C. & Jasiak, J., 2012. "Granularity adjustment for default risk factor model with cohorts," Journal of Banking & Finance, Elsevier, vol. 36(5), pages 1464-1477.
  10. Christian Schmieder & Maher Hasan & Claus Puhr, 2011. "Next Generation Balance Sheet Stress Testing," IMF Working Papers 11/83, International Monetary Fund.

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