Provide a model for calculating the economic capital of bank loan portfolio and compare it with regulatory capital

Document Type : Original Article

Authors

1 PhD student in international Finance, Department of Finance, Central Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Finance, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

3 Department of Finance, Central Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract

The purpose of this article is to provide a model for calculating the economic capital of a bank loan portfolio and compare the obtained results with regulatory capital based on Basel II Models. The widely used asset value approach is used to model the default correlation. The method of estimating the parameters is based on the Method of moments and the method of maximum likelihood. Finally, the economic capital is calculated based on the distribution of losses obtained from the Monte Carlo simulation method. According to research hypothesis, calculated economic capital is compared with calculated regulatory capital based on Basel II Models. Regarding obtained results regulatory capital is more than economic capital. Considering the difference between economic capital and regulatory capital in the selected portfolio, it is not sufficient to rely on regulatory capital to assess banks’ risk. In order to have an accurate assessment of banks’ risk, economic capital must be calculated with proper modeling.

Keywords

References

  • Souri, Ali (2015), Econometrics with the use of Eviews and Stata. Tehran: Farhangshenasi Publishing, fourth edition.
  • Moshiri,S, Abdolshah, F, (2017),Stress test, Iran banking industry the probability of default test with credit portfolio approach, Quarterly Journal of Economic Research, No. 66, 23-54
  • Shakeri et al. (2011), Credit portfolio loss distribution modeling using auxiliary predictor variables and the concept of frailty, Master Thesis, Zanjan Advanced Studies University
  • Bouteille, S., P, Coogan. (2013),” The Handbook of Credit Risk Management”, England: John Wiley & Sons.
  • Bellini, T. (2019).” IFRS9 and CECL Credit Risk Modeling and Validation”, London:
  • Bandyopadhyay, A. (2016),” Managing Portfolio Credit Risk in Banks “India: Cambridge.
  • Basel Committee on Banking Supervision (2006), International Convergence of Capital Measurement and Capital Standards-A, Revised Framework-Comprehensive Version, June
  • Basel Committee on Banking Supervision (2010) Basel III: A global regulatory framework for more resilient banks and banking systems, Bank for International Settlements, BCBS publications
  • Basel Committee on Banking Supervision (2011) Basel: A Global Regulatory Framework for More Resilient Banks and Banking Systems (revised), Bank for International Settlements,
  • Golin, J., P, Delhaise (2013), “The Bank Credit Analysis Handbook”, Singapore: John Wiley & Sons.
  • Iscoe, A., O, Romanko (2012), “Portfolio credit-risk optimization”, Journal of Banking & Finance, PP: 1604-1615.
  • Klassen, T., I, Eeghen (2009), “Economic Capital”, California: Elsevier.
  • Loffler, G., P, Posch (2011),” Credit Risk Modeling Using Excel and VBA”, 2thed, England: John Wiley & Sons.
  • Resti, A., A, Sironi (2007),” Risk Management and Shareholders Value in Banking”, England: John Wiley & Sons.
  • Van Gestel, T., B, Baesens (2009),” Credit Risk Management”, Oxford: Oxford University Press
  • Valuing a Bank IAS/IFRS and Basel II. Euromoney Instutional Investor 2005.
  • Basel Committee on Banking Supervision, Bael September 2000, “Principles for the Management of Credit Risk”.
  • Eluerkhaoui, Y., (2017). “Credit Correlation”, London: Palgrave macmillane.
  • Gordy, M. (2001), “A risk factor model foundation for rating-based bank capital rules”. Draft, February 2001.
  • Carey, M. (2000), “Dimensions of Credit Risk and their Relationship to Economic Capital Requirements”, NBER Working Paper, PP: 7629.
  • Gordy, Michael B., (2000).” A Comparative Anatomy of Credit Risk Models”, Journal of banking & Finance, PP:119-149
  • Engle, R., (2002)” Dynamic Conditional Correlation – A Simple Class of Multivariate GARCH Models”, Journal of Business and Economic Statistics 20(3), PP: 339-350.
  • Accornero, M., G, Cascarini, and Parlapiano, (2017),” Credit risk in banks’ exposures to non‐financial firms”, Journal of European Financial Management. vol 24,PP: 775-791
  • Glasserman, P., L, Jingyi (2005),” Importance sampling for Portfolio Credit Risk”, Management Science vol 5, PP: 1643-1656.
  • Nagpal, K., R., Bahar (1999),” An Analytical Approach for Credit Risk Analysis under Correlated Defaults”, Credit Metrics Monitor, PP: 51-79.
  • Bajaj, R. (2010), “Default Correlation Estimates for Indian Corporate”, Journal of Paradigm, vol 14,PP: 42-52

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