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Measuring bank capital requirements through Dynamic Factor analysis

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Author Info
Andrea Cipollini ()
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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Publisher 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
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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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Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation
G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Mortgages
G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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  1. Samuel Hanson & M. Hashem Pesaran & Til Schuermann, 2005. "Firm Heterogeneity and Credit Risk Diversification," CESifo Working Paper Series CESifo Working Paper No. , CESifo Group Munich. [Downloadable!]
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  2. Philipp J. Schönbucher, 2000. "Factor Models for Portofolio Credit Risk," Bonn Econ Discussion Papers bgse16_2001, University of Bonn, Germany. [Downloadable!]
  3. 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. [Downloadable!] (restricted)
  4. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August. [Downloadable!] (restricted)
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  5. Edward I. Altman & Brooks Brady & Andrea Resti & Andrea Sironi, 2005. "The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications," Journal of Business, University of Chicago Press, vol. 78(6), pages 2203-2228, November. [Downloadable!]
  6. 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. [Downloadable!] (restricted)
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  7. Carlos González-Aguado & Max Bruche, 2006. "Recovery Rates, Default Probabilities and the Credit Cycle," FMG Discussion Papers dp572, Financial Markets Group. [Downloadable!] (restricted)
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  8. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January. [Downloadable!] (restricted)
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