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Efficient estimation of unconditional capital by Monte Carlo simulation

Author

Listed:
  • Ferrer, Alex
  • Casals, José
  • Sotoca, Sonia

Abstract

We address the problem of determining the unconditional capital required by a credit portfolio using Monte Carlo simulation. By elaborating on a tractable analytical framework, we propose a new efficient simulation algorithm that overweights recession periods, which are the most important periods for determining the final capital figure, thereby improving its efficiency for a given number of simulations. We discuss the optimality and practical advantages of this algorithm. We also conduct an empirical analysis based on American charge-off data, which shows that the proposed algorithm achieves remarkable improvements in efficiency, without introducing any bias and at a negligible implementation cost.

Suggested Citation

  • Ferrer, Alex & Casals, José & Sotoca, Sonia, 2016. "Efficient estimation of unconditional capital by Monte Carlo simulation," Finance Research Letters, Elsevier, vol. 16(C), pages 75-84.
  • Handle: RePEc:eee:finlet:v:16:y:2016:i:c:p:75-84
    DOI: 10.1016/j.frl.2015.10.010
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    References listed on IDEAS

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    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. 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.
    3. Grundke, Peter, 2009. "Importance sampling for integrated market and credit portfolio models," European Journal of Operational Research, Elsevier, vol. 194(1), pages 206-226, April.
    4. Rodriguez, Adolfo & Trucharte, Carlos, 2007. "Loss coverage and stress testing mortgage portfolios: A non-parametric approach," Journal of Financial Stability, Elsevier, vol. 3(4), pages 342-367, December.
    5. Koopman, Siem Jan & Lucas, Andre & Klaassen, Pieter, 2005. "Empirical credit cycles and capital buffer formation," Journal of Banking & Finance, Elsevier, vol. 29(12), pages 3159-3179, December.
    6. Bonfim, Diana, 2009. "Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 281-299, February.
    7. Marcucci, Juri & Quagliariello, Mario, 2009. "Asymmetric effects of the business cycle on bank credit risk," Journal of Banking & Finance, Elsevier, vol. 33(9), pages 1624-1635, September.
    8. Gordy, Michael B., 2003. "A risk-factor model foundation for ratings-based bank capital rules," Journal of Financial Intermediation, Elsevier, vol. 12(3), pages 199-232, July.
    9. Bangia, Anil & Diebold, Francis X. & Kronimus, Andre & Schagen, Christian & Schuermann, Til, 2002. "Ratings migration and the business cycle, with application to credit portfolio stress testing," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 445-474, March.
    10. Chan, Joshua C.C. & Kroese, Dirk P., 2010. "Efficient estimation of large portfolio loss probabilities in t-copula models," European Journal of Operational Research, Elsevier, vol. 205(2), pages 361-367, September.
    11. Carey, Mark, 2002. "A guide to choosing absolute bank capital requirements," Journal of Banking & Finance, Elsevier, vol. 26(5), pages 929-951, May.
    12. Thomas Breuer & Martin Jandacka & Klaus Rheinberger & Martin Summer, 2009. "How to Find Plausible, Severe and Useful Stress Scenarios," International Journal of Central Banking, International Journal of Central Banking, vol. 5(3), pages 205-224, September.
    13. Halis Sak & Wolfgang Hörmann, 2012. "Fast simulations in credit risk," Quantitative Finance, Taylor & Francis Journals, vol. 12(10), pages 1557-1569, October.
    14. Pederzoli, Chiara & Torricelli, Costanza, 2005. "Capital requirements and business cycle regimes: Forward-looking modelling of default probabilities," Journal of Banking & Finance, Elsevier, vol. 29(12), pages 3121-3140, December.
    15. Frey, Rudiger & McNeil, Alexander J., 2002. "VaR and expected shortfall in portfolios of dependent credit risks: Conceptual and practical insights," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1317-1334, July.
    16. Paul Glasserman & Jingyi Li, 2005. "Importance Sampling for Portfolio Credit Risk," Management Science, INFORMS, vol. 51(11), pages 1643-1656, November.
    17. Mark S. Carey, 2002. "A guide to choosing absolute bank capital requirements," International Finance Discussion Papers 726, Board of Governors of the Federal Reserve System (U.S.).
    18. Sandro Merino & Mark Nyfeler, 2004. "Applying importance sampling for estimating coherent credit risk contributions," Quantitative Finance, Taylor & Francis Journals, vol. 4(2), pages 199-207.
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    More about this item

    Keywords

    Capital estimation; Charge-off; Credit risk; Monte Carlo simulation; Unconditional capital;

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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