Abstract
Operational risk is one of the most important risks for Chinese commercial banks, and brings huge losses to Chinese commercial banks recent years. Using the public reported operational loss data from 1997 to 2005 of Chinese commercial banks, we simulate the operational loss distribution, find that loss frequency can be seen as Poisson distribution and the logarithm of loss is normal distribution. In accordance with the confidence level required by Basel II, aggregated loss distributions and operational Value-at-Risks (OpVaR) are calculated by Monte Carlo Simulation. Comparing with the real loss, this result is credible. We also calculate the economic capital by the VaR 99.9, and it maybe help the banks to allocate appropriate their economic capital.
This research has been partially supported by a grant from National Natural Science Foundation of China (#70531040), the President Fund of Chinese Academy of Sciences (yzjj946) and 973 Project( #2004CB720103), Ministry of Science and Technology, China.
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References
Basel Committee on Banking Supervision. Operational Risk, Consultative Document. Basel (September 2001), http://www.bis.org
The Basel Committee on Banking Supervision Bank for International Settlements CH-4002 Basel. Third consultative paper(CP3) on the New Basel Capital Accord. Switzerland, July 30 (2003)
Hubner, G., Peters, J.-P., Plunus, S.: Measuring operational risk in financial institutions: Contribution of credit risk modeling (March 2005)
Helbok, G., Wagner, C.: Corporate financial disclosure on operational risk in the banking industry. Working Paper (September 2004)
Cornalba, C., Giudici, P.: Statistical models for operational risk management. Physical A 338, 166–172 (2004)
Gao, L.J., Li, J.P., Chen, J.M., Wang, S.P.: A New Assessment of Operational Risk of commercial bank: OpRisk+ Model. Chinese Journal of Management Science(S) 13, 185–188 (2005) (in Chinese)
Patrick, F., Eric, R., Jordan, J.: Implications of alternative operational risk modeling techniques. NBER Working Paper No. 11103 (June 2004), http://papers.nber.org/papers/W11103
Alexander, C.: Statistical models of operational loss. In: Operational Risk. Regulation, Analysis and Management, FT Prentice Hall Financial Times, pp. 129–170 (2003)
Peters, J.P., Crama, Y., Hubner, G.: Basel II project: computation of OpVaR, Working Paper (2003), HEC Management School, University of Liège
Basel II: International Convergence of Capital Measurement and Capital Standards: a Revised Framework, Basel Committee Publications (June 2004)
Shi, Y., Peng, Y., Kou, G., Chen, Z.: Classifying Credit Card Accounts for Business Intelligence and Decision Making: A Multiple-Criteria Quadratic Programming Approach. International Journal of Information Technology and Decision Making 4(4), 1–19 (2005)
Kou, G., Peng, Y., Shi, Y., Wise, M., Xu, W.X.: Discovering Credit Cardholders’ Behavior by Multiple Criteria Linear Programming. Annals of Operations Research 135(1), 261–274 (2005)
Li, J.P., Liu, J.L., Xu, W.X., Shi, Y.: Support Vector Machines Approach to Credit Assessment. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3039, pp. 892–899. Springer, Heidelberg (2004)
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Gao, L., Li, J., Chen, J., Xu, W. (2006). Assessment the Operational Risk for Chinese Commercial Banks. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758549_70
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DOI: https://doi.org/10.1007/11758549_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34385-1
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