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Modelling Operational Risk Losses with Graphical Models and Copula Functions

Author

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  • Danae Politou

    (Citi
    University of Pavia)

  • Paolo Giudici

    (University of Pavia)

Abstract

The management of Operational Risk has been a difficult task due to the lack of data and the high number of variables. In this project, we treat operational risks as multivariate variables. In order to model them, copula functions are employed, which are a widely used tool in finance and engineering for building flexible joint distributions. The purpose of this research is to propose a new methodology for modelling Operational Risks and estimating the required capital. It combines the use of graphical models and the use of copula functions along with hyper-Markov law. Historical loss data of an Italian bank is used, in order to explore the methodology’s behaviour and its potential benefits.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:metcap:v:11:y:2009:i:1:d:10.1007_s11009-008-9083-5
    DOI: 10.1007/s11009-008-9083-5
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    References listed on IDEAS

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    1. 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.
    2. Yamai, Yasuhiro & Yoshiba, Toshinao, 2002. "Comparative Analyses of Expected Shortfall and Value-at-Risk (3): Their Validity under Market Stress," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 20(3), pages 181-237, 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, July.
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    Cited by:

    1. 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.
    2. Silvia Figini & Lijun Gao & Paolo Giudici, 2013. "Bayesian operational risk models," DEM Working Papers Series 047, University of Pavia, Department of Economics and Management.
    3. Denuit, Michel & Robert, Christian Y., 2020. "Conditional mean risk sharing for dependent risks using graphical models," LIDAM Discussion Papers ISBA 2020029, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

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