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(Non-)robustness of maximum likelihood estimators for operational risk severity distributions

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  • Sonja Huber

Abstract

The quality of operational risk data sets suffers from missing or contaminated data points. This may lead to implausible characteristics of the estimates. Outliers, especially, can make a modeler's task difficult and can result in arbitrarily large capital charges. Robust statistics provides ways to deal with these problems as well as measures for the reliability of estimators. We show that using maximum likelihood estimation can be misleading and unreliable assuming typical operational risk severity distributions. The robustness of the estimators for the Generalized Pareto distribution, and the Weibull and Lognormal distributions is measured considering both global and local reliability, which are represented by the breakdown point and the influence function of the estimate.

Suggested Citation

  • Sonja Huber, 2010. "(Non-)robustness of maximum likelihood estimators for operational risk severity distributions," Quantitative Finance, Taylor & Francis Journals, vol. 10(8), pages 871-882.
    • Handle: RePEc:taf:quantf:v:10:y:2010:i:8:p:871-882
    DOI: 10.1080/14697680903159240
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    Cited by:

    1. Muhammad Aslam Mohd Safari & Nurulkamal Masseran & Muhammad Hilmi Abdul Majid, 2020. "Robust Reliability Estimation for Lindley Distribution—A Probability Integral Transform Statistical Approach," Mathematics, MDPI, vol. 8(9), pages 1-21, September.
    2. Zhou, Xiaoping & Durfee, Antonina V. & Fabozzi, Frank J., 2016. "On stability of operational risk estimates by LDA: From causes to approaches," Journal of Banking & Finance, Elsevier, vol. 68(C), pages 266-278.

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