IDEAS home Printed from https://ideas.repec.org/p/mod/recent/010.html
   My bibliography  Save this paper

Measuring bank capital requirements through Dynamic Factor analysis

Author

Listed:
  • Andrea Cipollini
  • Giuseppe Missaglia

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.

Suggested Citation

  • Andrea Cipollini & Giuseppe Missaglia, 2008. "Measuring bank capital requirements through Dynamic Factor analysis," Center for Economic Research (RECent) 010, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    • Handle: RePEc:mod:recent:010
    as

    Download full text from publisher

    File URL: http://155.185.68.2/campusone/web_dep/Recentpaper/recent-wp10.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. 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 449-470, May.
    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. 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.
    6. Schönbucher, Philipp J., 2000. "Factor Models for Portofolio Credit Risk," Bonn Econ Discussion Papers 16/2001, University of Bonn, Bonn Graduate School of Economics (BGSE).
    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. Hanson, Samuel G. & Pesaran, M. Hashem & Schuermann, Til, 2008. "Firm heterogeneity and credit risk diversification," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 583-612, September.
    9. Lippi, Marco & Reichlin, Lucrezia & Forni, Mario, 2003. "Opening the Black Box: Structural Factor Models versus Structural VARs," CEPR Discussion Papers 4133, C.E.P.R. Discussion Papers.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tang, Dragon Yongjun & Yan, Hong, 2010. "Market conditions, default risk and credit spreads," Journal of Banking & Finance, Elsevier, vol. 34(4), pages 743-753, April.
    2. Mora, Nada, 2015. "Creditor recovery: The macroeconomic dependence of industry equilibrium," Journal of Financial Stability, Elsevier, vol. 18(C), pages 172-186.
    3. Jean-David Fermanian, 2020. "On the Dependence between Default Risk and Recovery Rates in Structural Models," Annals of Economics and Statistics, GENES, issue 140, pages 45-82.
    4. Nada Mora, 2012. "What determines creditor recovery rates?," Economic Review, Federal Reserve Bank of Kansas City, vol. 97(Q II).
    5. Andrea Cipollini & Giuseppe Missaglia, 2007. "Dynamic Factor analysis of industry sector default rates and implication for Portfolio Credit Risk Modelling," Center for Economic Research (RECent) 007, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    6. Pascal François, 2019. "The Determinants of Market-Implied Recovery Rates," Risks, MDPI, vol. 7(2), pages 1-15, May.
    7. Daniel Rösch & Harald Scheule, 2014. "Forecasting Mortgage Securitization Risk Under Systematic Risk and Parameter Uncertainty," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 81(3), pages 563-586, September.
    8. Bernd Schwaab & Siem Jan Koopman & André Lucas, 2017. "Global Credit Risk: World, Country and Industry Factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 296-317, March.
    9. Wang, Hong & Forbes, Catherine S. & Fenech, Jean-Pierre & Vaz, John, 2020. "The determinants of bank loan recovery rates in good times and bad – New evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 875-897.
    10. Thamayanthi Chellathurai, 2017. "Probability Density Of Recovery Rate Given Default Of A Firm’S Debt And Its Constituent Tranches," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(04), pages 1-34, June.
    11. Rösch, Daniel & Scheule, Harald, 2009. "The Empirical Relation between Credit Quality, Recovery and Correlation," Hannover Economic Papers (HEP) dp-418, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    12. Tomas Konecny & Jakub Seidler & Aelta Belyaeva & Konstantin Belyaev, 2017. "The Time Dimension of the Links Between Loss Given Default and the Macroeconomy," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 67(6), pages 462-491, October.
    13. Wu, Yang-Che & Chung, San-Lin, 2010. "Catastrophe risk management with counterparty risk using alternative instruments," Insurance: Mathematics and Economics, Elsevier, vol. 47(2), pages 234-245, October.
    14. Annaert, Jan & De Ceuster, Marc & Van Roy, Patrick & Vespro, Cristina, 2013. "What determines Euro area bank CDS spreads?," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 444-461.
    15. J. Baixauli & Susana Alvarez, 2012. "Implied Severity Density Estimation: An Extended Semiparametric Method to Compute Credit Value at Risk," Computational Economics, Springer;Society for Computational Economics, vol. 40(2), pages 115-129, August.
    16. Albert Cohen & Nick Costanzino, 2017. "Bond and CDS Pricing via the Stochastic Recovery Black-Cox Model," Risks, MDPI, vol. 5(2), pages 1-28, April.
    17. Carey, Mark & Gordy, Michael B., 2021. "The bank as Grim Reaper: Debt composition and bankruptcy thresholds," Journal of Financial Economics, Elsevier, vol. 142(3), pages 1092-1108.
    18. Bank for International Settlements, 2011. "Portfolio and risk management for central banks and sovereign wealth funds," BIS Papers, Bank for International Settlements, number 58.
    19. Alexander Becker & Alexander F. R. Koivusalo & Rudi Schafer, 2012. "Empirical Evidence for the Structural Recovery Model," Papers 1203.3188, arXiv.org.
    20. Ethan Cohen-Cole, 2007. "Asset liquidity, debt valuation and credit risk," Supervisory Research and Analysis Working Papers QAU07-5, Federal Reserve Bank of Boston.

    More about this item

    Keywords

    Dynamic Factor Model; Forecasting; Stochastic Simulation; Risk Management; Banking;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mod:recent:010. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/demodit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.