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Statistical models for operational risk management

Author

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  • Cornalba, Chiara
  • Giudici, Paolo

Abstract

The Basel Committee on Banking Supervision has released, in the last few years, recommendations for the correct determination of the risks to which a banking organization is subject. This concerns, in particular, operational risks, which are all those management events that may determine unexpected losses. It is necessary to develop valid statistical models to measure and, consequently, predict, such operational risks. In the paper we present the possible approaches, including our own proposal, which is based on Bayesian networks.

Suggested Citation

  • Cornalba, Chiara & Giudici, Paolo, 2004. "Statistical models for operational risk management," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(1), pages 166-172.
    • Handle: RePEc:eee:phsmap:v:338:y:2004:i:1:p:166-172
    DOI: 10.1016/j.physa.2004.02.039
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    Citations

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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. 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.
    3. Marco Bardoscia & Roberto Bellotti, 2012. "A Dynamical Approach to Operational Risk Measurement," Papers 1202.2532, arXiv.org.
    4. Paola Cerchiello & Paolo Giudici, 2013. "Bayesian Credit Ratings (new version)," DEM Working Papers Series 030, University of Pavia, Department of Economics and Management.
    5. Sinemis Zengin & Serhat Yuksel, 2016. "A Comparison of the Views of Internal Controllers/Auditors and Branch/Call Center Personnel of the Banks for Operational Risk: A Case for Turkish Banking Sector," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 5(4), pages 10-29, July.
    6. Danae Politou & Paolo Giudici, 2009. "Modelling Operational Risk Losses with Graphical Models and Copula Functions," Methodology and Computing in Applied Probability, Springer, vol. 11(1), pages 65-93, March.
    7. Paolo Giudici, 2015. "Scorecard models for operations management," International Journal of Data Science, Inderscience Enterprises Ltd, vol. 1(1), pages 96-101.
    8. Silvia Figini & Lijun Gao & Paolo Giudici, 2013. "Bayesian operational risk models," DEM Working Papers Series 047, University of Pavia, Department of Economics and Management.
    9. 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.
    10. Mizgier, Kamil J. & Hora, Manpreet & Wagner, Stephan M. & Jüttner, Matthias P., 2015. "Managing operational disruptions through capital adequacy and process improvement," European Journal of Operational Research, Elsevier, vol. 245(1), pages 320-332.
    11. 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.
    12. Bojaj, Martin M. & Muhadinovic, Milica & Bracanovic, Andrej & Mihailovic, Andrej & Radulovic, Mladen & Jolicic, Ivan & Milosevic, Igor & Milacic, Veselin, 2022. "Forecasting macroeconomic effects of stablecoin adoption: A Bayesian approach," Economic Modelling, Elsevier, vol. 109(C).
    13. Paola Cerchiello & Paolo Giudici, 2012. "Bayesian Credit Rating Assessment," DEM Working Papers Series 019, University of Pavia, Department of Economics and Management.
    14. Borunda, Mónica & Jaramillo, O.A. & Reyes, Alberto & Ibargüengoytia, Pablo H., 2016. "Bayesian networks in renewable energy systems: A bibliographical survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 32-45.

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