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A threshold based approach to merge data in financial risk management

Author

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  • Silvia Figini
  • Paolo Giudici
  • Pierpaolo Uberti

Abstract

According to the last proposals by the Basel Committee, banks are allowed to use statistical approaches for the computation of their capital charge covering financial risks such as credit risk, market risk and operational risk. It is widely recognized that internal loss data alone do not suffice to provide accurate capital charge in financial risk management, especially for high-severity and low-frequency events. Financial institutions typically use external loss data to augment the available evidence and, therefore, provide more accurate risk estimates. Rigorous statistical treatments are required to make internal and external data comparable and to ensure that merging the two databases leads to unbiased estimates. The goal of this paper is to propose a correct statistical treatment to make the external and internal data comparable and, therefore, mergeable. Such methodology augments internal losses with relevant, rather than redundant, external loss data.

Suggested Citation

  • Silvia Figini & Paolo Giudici & Pierpaolo Uberti, 2010. "A threshold based approach to merge data in financial risk management," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(11), pages 1815-1824.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:11:p:1815-1824
    DOI: 10.1080/02664760903164921
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    References listed on IDEAS

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    1. Saita, Francesco, 2007. "Value at Risk and Bank Capital Management," Elsevier Monographs, Elsevier, edition 1, number 9780123694669.
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    Cited by:

    1. Tyrone Lin & Chia-Chi Lee & Yu-Chuan Kuan, 2013. "The optimal operational risk capital requirement by applying the advanced measurement approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 21(1), pages 85-101, January.
    2. 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.
    3. Silvia FIGINI & Ron S. KENETT & Silvia SALINI, 2010. "Integrating operational and financial risk assessments," Departmental Working Papers 2010-02, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.

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