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A tale of tails: an empirical analysis of loss distribution models for estimating operational risk capital

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  • Kabir Dutta
  • Jason Perry
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    Abstract

    Operational risk is being considered as an important risk component for financial institutions as evinced by the large sums of capital that are allocated to mitigate this risk. Therefore, risk measurement is of paramount concern for the purposes of capital allocation, hedging, and new product development for risk mitigation. We perform a comprehensive evaluation of commonly used methods and introduce new techniques to measure this risk with respect to various criteria. We find that our newly introduced techniques perform consistently better than the other models we tested.

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    Bibliographic Info

    Paper provided by Federal Reserve Bank of Boston in its series Working Papers with number 06-13.

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    Date of creation: 2006
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    Handle: RePEc:fip:fedbwp:06-13

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    Keywords: Risk management;

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    References

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    1. Kenneth A. Froot, 2001. "Bank capital and risk management: operational risks in context," Conference Series ; [Proceedings], Federal Reserve Bank of Boston.
    2. Badrinath, S G & Chatterjee, Sangit, 1988. "On Measuring Skewness and Elongation in Common Stock Return Distributions: The Case of the Market Index," The Journal of Business, University of Chicago Press, vol. 61(4), pages 451-72, October.
    3. Philippe Artzner & Freddy Delbaen & Jean-Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228.
    4. Bookstaber, Richard M & McDonald, James B, 1987. "A General Distribution for Describing Security Price Returns," The Journal of Business, University of Chicago Press, vol. 60(3), pages 401-24, July.
    5. Kabir K. Dutta & David F. Babbel, 2002. "On Measuring Skewness and Kurtosis in Short Rate Distributions: The Case of the US Dollar London Inter Bank Offer Rates," Center for Financial Institutions Working Papers 02-25, Wharton School Center for Financial Institutions, University of Pennsylvania.
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    Cited by:
    1. Wang, Zongrun & Wang, Wuchao & Chen, Xiaohong & Jin, Yanbo & Zhou, Yanju, 2012. "Using BS-PSD-LDA approach to measure operational risk of Chinese commercial banks," Economic Modelling, Elsevier, vol. 29(6), pages 2095-2103.
    2. Gareth W. Peters & Rodrigo S. Targino & Pavel V. Shevchenko, 2013. "Understanding Operational Risk Capital Approximations: First and Second Orders," Papers 1303.2910, arXiv.org.
    3. Jarrow, Robert A., 2008. "Operational risk," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 870-879, May.
    4. Buch-Kromann, Tine & Guillén, Montserrat & Linton, Oliver & Nielsen, Jens Perch, 2011. "Multivariate density estimation using dimension reducing information and tail flattening transformations," Insurance: Mathematics and Economics, Elsevier, vol. 48(1), pages 99-110, January.
    5. Chernobai, Anna & Yildirim, Yildiray, 2008. "The dynamics of operational loss clustering," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2655-2666, December.
    6. Albrecht, Peter & Schwake, Edmund & Winter, Peter, 2007. "Quantifizierung operationeller Risiken: Der Loss Distribution Approach," German Risk and Insurance Review (GRIR), University of Cologne, Department of Risk Management and Insurance, vol. 3(1), pages 1-45.

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