Modeling Truncated Loss Data of Operational Risk in E-banking

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Maryam Pirouz 1,* Maziar Salahi 2

1. Department of Computer Engineering, International Unit, University of Guilan, Rasht, Guilan, Iran

2. Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht, Guilan, Iran

* Corresponding author.


Received: 21 Jan. 2013 / Revised: 10 May 2013 / Accepted: 26 Jul. 2013 / Published: 8 Nov. 2013

Index Terms

E-banking, Operational Risk, Truncated Data, Basel Committee


Operational risk is an important risk component for financial institutions, especially in E-banking. Large amount of capital that are assigned to decrease this risk are evidence to this subject. One of the most important factors for modeling operational risk to estimate capital charge is loss data collections of banks. But sometimes for reasons like decreasing the costs, banks save only the losses above some determined thresholds at their database. For achieving accurate capital charge, this threshold should be considered in determining capital charge. This paper focuses on modeling truncated loss data above some given threshold. We discuss several statistical methods for modeling truncated data, and suggest the best one for modeling truncated loss data. We have tested our suggested model for some operational loss data samples. Our results indicate that our approach can be useful for increasing accuracy of estimating operational risk capital charge in E-banking.

Cite This Paper

Maryam Pirouz, Maziar Salahi, "Modeling Truncated Loss Data of Operational Risk in E-banking", International Journal of Information Technology and Computer Science(IJITCS), vol.5, no.12, pp.64-69, 2013. DOI:10.5815/ijitcs.2013.12.08


[1]J. Kondabaghil, Risk Management in Electronic Banking, John Wiley & Sons (Asia) Pte Ltd., 2007, pp. 11-13.

[2]Basel Committee on Banking Supervision, risk management principles for Electronic banking, 2003.

[3]Basel Committee on Banking Supervision, International Convergence of Capital Measurement and Capital Standards - A Revised Framework Comprehensive Version, 2006. 

[4]A. Chapelle, Y. Crama, and G. Hu¨bner, Practical Methods for Measuring and Managing Operational Risk in the Financial Sector: A Clinical Study, Journal of Banking &Finance, 2008, 32, pp. 1049-1061.

[5]V. Chves-Demoulin, and P. Emberchts, Quantitative models for operational risk: Extremes, dependence and aggregation. Journal of Banking and Fiance, 2006, 30, pp. 2635-2658.

[6]R. Coleman, Operational risk modelling for extremes, Operational Risk, pp. 6–9, 2003.

[7]P. de Fontnuvelle, E. Rosenbrgren, and J. Jordan, Implications of alternative operational risk modeling techniques. Working paper, Federal Reserve Bank of Boston, 2004.

[8]P. de Fontnouvelle, J. Jordan, V. DeJesus-Ureff, and E. Rosengren, Capital and risk: New evidence onimplications of large operational losses. Working paper, Federal Reserve Bank of Boston, 2005. 

[9]H. Dahen, and G. Dionne, Scaling Models for the Severity and Frequency of External Operational Loss Data, Journal of Banking & Finance, 2010, 46, pp. 3545-3568.

[10]S. Ebnother, P. Vanini, A. McNeil, and P. Antolinez-Fehr, Modeling operational risk. Working paper, ETH Zurich, 2001.

[11]P. Embrechts, and G.Puccetti, Aggregation operational risk across matrix structured loss data. Journal of Operational Risk, 2004, 3(2), pp. 29-44.

[12]F. Lindskog, and A. McNeil, Common Poisson shock models: Applications to insurance and credit risk modeling. ASTIN Bulletin, 2003, 33 (2), pp. 209–238.

[13]M. Moscadelli, “The modelling of operational risk: Experience with the analysis of the data collected by the basel committee. Technical Report 517, Banca d’Italia, 2004.

[14]BIS 2003, The 2002 Loss Data Collection Exercise for Operational Risk: Summary of the Data Collected,

[15]A.S. Chernobai,S. T. Rachev, and F. J. Fabozzi, Operational Risk, A Guide to Basel II Capital Requirements, Model and Analysis, John Wiley & Sons, 2007, pp. 183- 186.