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A Bayesian approach to estimate the marginal loss distributions in operational risk management

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  • Dalla Valle, L.
  • Giudici, P.

Abstract

One of the main problems in operational risk management is the lack of loss data, which affects the parameter estimates of the marginal distributions of the losses. The principal reason is that financial institutions only started to collect operational loss data a few years ago, due to the relatively recent definition of this type of risk. Considering this drawback, the employment of Bayesian methods and simulation tools could be a natural solution to the problem. The use of Bayesian methods allows us to integrate the scarce and, sometimes, inaccurate quantitative data collected by the bank with prior information provided by experts. An original proposal is a Bayesian approach for modelling operational risk and for calculating the capital required to cover the estimated risks. Besides this methodological innovation a computational scheme, based on Markov chain Monte Carlo simulations, is required. In particular, the application of the MCMC method to estimate the parameters of the marginals shows advantages in terms of a reduction of capital charge according to different choices of the marginal loss distributions.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:6:p:3107-3127
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    References listed on IDEAS

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    Cited by:

    1. Iñaki Aldasoro & Leonardo Gambacorta & Paolo Giudici & Thomas Leach, 2023. "Operational and Cyber Risks in the Financial Sector," International Journal of Central Banking, International Journal of Central Banking, vol. 19(5), pages 340-402, December.
    2. Paola Cerchiello & Paolo Giudici, 2014. "How to measure the quality of financial tweets," DEM Working Papers Series 069, University of Pavia, Department of Economics and Management.
    3. Fantazzini, Dean, 2008. "Econometric Analysis of Financial Data in Risk Management (continuation). Section III: Managing Operational Risk," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 11(3), pages 87-122.
    4. Yuan Hong & Shaojian Qu, 2024. "Beyond Boundaries: The AHP-DEA Model for Holistic Cross-Banking Operational Risk Assessment," Mathematics, MDPI, vol. 12(7), pages 1-18, March.
    5. Ramírez-Cobo, Pepa & Carrizosa, Emilio & Lillo, Rosa E., 2021. "Analysis of an aggregate loss model in a Markov renewal regime," Applied Mathematics and Computation, Elsevier, vol. 396(C).
    6. Facchinetti, Silvia & Osmetti, Silvia Angela & Tarantola, Claudia, 2023. "Network models for cyber attacks evaluation," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    7. Paola Cerchiello & Paolo Giudici, 2013. "H Index: A Statistical Proposal," DEM Working Papers Series 039, University of Pavia, Department of Economics and Management.
    8. Paola Cerchiello & Paolo Giudici, 2014. "On a statistical h index," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 299-312, May.
    9. Paolo Giudici, 2015. "Scorecard models for operations management," International Journal of Data Science, Inderscience Enterprises Ltd, vol. 1(1), pages 96-101.
    10. Otranto, Edoardo, 2008. "Clustering heteroskedastic time series by model-based procedures," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4685-4698, June.
    11. Silvia Figini & Lijun Gao & Paolo Giudici, 2013. "Bayesian operational risk models," DEM Working Papers Series 047, University of Pavia, Department of Economics and Management.
    12. 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.
    13. Lu, Zhaoyang, 2011. "Modeling the yearly Value-at-Risk for operational risk in Chinese commercial banks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(4), pages 604-616.
    14. Lu Wei & Jianping Li & Xiaoqian Zhu, 2018. "Operational Loss Data Collection: A Literature Review," Annals of Data Science, Springer, vol. 5(3), pages 313-337, September.
    15. Francesca Greselin & Fabio Piacenza & Ričardas Zitikis, 2019. "Practice Oriented and Monte Carlo Based Estimation of the Value-at-Risk for Operational Risk Measurement," Risks, MDPI, vol. 7(2), pages 1-20, May.
    16. Xu, Chi & Zheng, Chunling & Wang, Donghua & Ji, Jingru & Wang, Nuan, 2019. "Double correlation model for operational risk: Evidence from Chinese commercial banks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 327-339.
    17. 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.
    18. 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.
    19. Paola Cerchiello & Paolo Giudici, 2015. "A Bayesian h-index: how to measure research impact," DEM Working Papers Series 102, University of Pavia, Department of Economics and Management.

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