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Computation of Operational Risk for Financial Institutions


  • Ming-Tao CHUNG

    () (Department of Management Information Systems, National Chengchi University, Taiwan (R.O.C.))

  • Ming-Hua HSIEH

    () (Department of Risk Management and Insurance, National Chengchi University, Taiwan (R.O.C))

  • Yan-Ping CHI

    () (Department of Management Information Systems, National Chengchi University, Taiwan (R.O.C))


Quantification of operational risk has led to significant concern regarding regulation in the financial industry. Basel Accord II and III for banks and Solvency II for insurers require insurance companies and banks to allocate capital for operation risk. Because the risk measure used for Basel regulatory capital purposes reflects a confidence level of 99.9% during one year and the loss distribution of operational risk has high skewness and kurtosis, it is almost infeasible to get an accurate estimate of such a risk measure if a crude Monte Carlo approach is used. Therefore, we develop a novel importance sampling method for estimating such a risk measure. Numerical results demonstrate that the proposed method is very efficient and robust. The main contribution of this method is to provide a feasible and flexible numerical approach that delivers highly accurate estimates of operational risk with a high confidence level and meets the high international regulatory standard for quantification of operational risk.

Suggested Citation

  • Ming-Tao CHUNG & Ming-Hua HSIEH & Yan-Ping CHI, 2017. "Computation of Operational Risk for Financial Institutions," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 77-87, September.
  • Handle: RePEc:rjr:romjef:v::y:2017:i:3:p:77-87

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    References listed on IDEAS

    1. Dominique Guegan & Bertrand Hassani & Cédric Naud, 2010. "An efficient threshold choice for operational risk capital computation," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00544342, HAL.
    2. Frachot, Antoine & Roncalli, Thierry & Salomon, Eric, 2004. "The Correlation Problem in Operational Risk," MPRA Paper 38052, University Library of Munich, Germany.
    3. Peter W. Glynn & Donald L. Iglehart, 1989. "Importance Sampling for Stochastic Simulations," Management Science, INFORMS, vol. 35(11), pages 1367-1392, November.
    4. Chapelle, Ariane & Crama, Yves & Hübner, Georges & Peters, Jean-Philippe, 2008. "Practical methods for measuring and managing operational risk in the financial sector: A clinical study," Journal of Banking & Finance, Elsevier, vol. 32(6), pages 1049-1061, June.
    5. Chavez-Demoulin, V. & Embrechts, P. & Neslehova, J., 2006. "Quantitative models for operational risk: Extremes, dependence and aggregation," Journal of Banking & Finance, Elsevier, vol. 30(10), pages 2635-2658, October.
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    More about this item


    operational risk; advanced measurement approaches; loss distribution approach; Monte Carlo simulation; variance reduction;

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques


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