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Measuring bank capital requirements through Dynamic Factor analysis

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  • Andrea Cipollini

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  • Giuseppe Missaglia

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Abstract

In this paper, using industry sector stock returns as proxies of firm asset values, we obtain bank capital requirements (through the cycle). This is achieved by Montecarlo simulation of a bank loan portfolio loss density. We depart from the Basel 2 analytical formula developed by Gordy (2003) for the computation of the economic capital by, first, allowing dynamic heterogeneity in the factor loadings, and, also, by accounting for stochastic dependent recoveries. Dynamic heterogeneity in the factor loadings is introduced by using dynamic forecast of a Dynamic Factor model fitted to a large dataset of macroeconomic credit drivers. The empirical findings show that there is a decrease in the degree of Portfolio Credit Risk, once we move from the Basel 2 analytic formula to the Dynamic Factor model specification.

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Bibliographic Info

Paper provided by University of Modena and Reggio E., Dept. of Economics in its series Center for Economic Research (RECent) with number 010.

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Length: pages 26
Date of creation: Feb 2008
Date of revision:
Handle: RePEc:mod:recent:010

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Web page: http://www.recent.unimore.it/
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Related research

Keywords: Dynamic Factor Model; Forecasting; Stochastic Simulation; Risk Management; Banking;

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  1. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-70, May.
  2. M. Hashem Pesaran & Til Schuermann & Björn-Jakob Treutler & Scott M. Weiner & April, . "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Center for Financial Institutions Working Papers 03-13, Wharton School Center for Financial Institutions, University of Pennsylvania.
  3. Philipp J. Schönbucher, 2000. "Factor Models for Portofolio Credit Risk," Bonn Econ Discussion Papers bgse16_2001, University of Bonn, Germany.
  4. Bruche, Max & González-Aguado, Carlos, 2010. "Recovery rates, default probabilities, and the credit cycle," Journal of Banking & Finance, Elsevier, vol. 34(4), pages 754-764, April.
  5. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
  6. Edward I. Altman & Brooks Brady & Andrea Resti & Andrea Sironi, 2005. "The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications," The Journal of Business, University of Chicago Press, vol. 78(6), pages 2203-2228, November.
  7. Acharya, Viral V. & Bharath, Sreedhar T. & Srinivasan, Anand, 2007. "Does industry-wide distress affect defaulted firms? Evidence from creditor recoveries," Journal of Financial Economics, Elsevier, vol. 85(3), pages 787-821, September.
  8. Samuel Hanson & M. Hashem Pesaran & Til Schuermann, 2005. "Firm Heterogeneity and Credit Risk Diversification," CESifo Working Paper Series 1531, CESifo Group Munich.
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Cited by:
  1. Petr Gapko & Martin Smid, 2012. "Dynamic Multi-Factor Credit Risk Model with Fat-Tailed Factors," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 62(2), pages 125-140, May.

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