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The Bayesian Approach to Capital Allocation at Operational Risk: A Combination of Statistical Data and Expert Opinion

Author

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  • Mohamed Habachi

    (Department of Management Sciences, University of Mohamed 5, Rabat 10052, Morocco)

  • Saâd Benbachir

    (Department of Management Sciences, University of Mohamed 5, Rabat 10052, Morocco)

Abstract

Operational risk management remains a major concern for financial institutions. Indeed, institutions are bound to manage their own funds to hedge this risk. In this paper, we propose an approach to allocate one’s own funds based on a combination of historical data and expert opinion using the loss distribution approach (LDA) and Bayesian logic. The results show that internal models are of great importance in the process of allocating one’s own funds, and the use of the Delphi method for modelling expert opinion is very useful in ensuring the reliability of estimates.

Suggested Citation

  • Mohamed Habachi & Saâd Benbachir, 2020. "The Bayesian Approach to Capital Allocation at Operational Risk: A Combination of Statistical Data and Expert Opinion," IJFS, MDPI, vol. 8(1), pages 1-25, February.
    • Handle: RePEc:gam:jijfss:v:8:y:2020:i:1:p:9-:d:320948
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    References listed on IDEAS

    as
    1. Dalla Valle, L. & Giudici, P., 2008. "A Bayesian approach to estimate the marginal loss distributions in operational risk management," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3107-3127, February.
    2. Weidong Tian & Azamat Abdymomunov & Ibrahim Ergen, 2017. "Tail Dependence and Systemic Risk in Operational Losses of the US Banking Industry," International Review of Finance, International Review of Finance Ltd., vol. 17(2), pages 177-204, June.
    3. Jan Dhaene & Andreas Tsanakas & Emiliano A. Valdez & Steven Vanduffel, 2012. "Optimal Capital Allocation Principles," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 79(1), pages 1-28, March.
    4. Luciana Dalla Valle, 2009. "Bayesian Copulae Distributions, with Application to Operational Risk Management," Methodology and Computing in Applied Probability, Springer, vol. 11(1), pages 95-115, March.
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