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Practice Oriented and Monte Carlo Based Estimation of the Value-at-Risk for Operational Risk Measurement

Author

Listed:
  • Francesca Greselin

    (Department of Statistics and Quantitative Methods, Milano-Bicocca University, 20126 Milano, Italy
    These authors contributed equally to this work.)

  • Fabio Piacenza

    (Group Operational and Reputational Risks, UniCredit S.p.A., 20154 Milano, Italy
    These authors contributed equally to this work.)

  • Ričardas Zitikis

    (School of Mathematical and Statistical Sciences, Western University, London, ON N6A 5B7, Canada
    These authors contributed equally to this work.
    Research of the third author has been supported by the Natural Sciences and Engineering Research Council of Canada under the title “From Data to Integrated Risk Management and Smart Living: Mathematical Modelling, Statistical Inference, and Decision Making,” as well as by a Mitacs Accelerate Award from the national research organization Mathematics of Information Technology and Complex Systems, Canada, in partnership with Sun Life Financial, under the title “Risk Aggregation Beyond the Normal Limits.”)

Abstract

We explore the Monte Carlo steps required to reduce the sampling error of the estimated 99.9% quantile within an acceptable threshold. Our research is of primary interest to practitioners working in the area of operational risk measurement, where the annual loss distribution cannot be analytically determined in advance. Usually, the frequency and the severity distributions should be adequately combined and elaborated with Monte Carlo methods, in order to estimate the loss distributions and risk measures. Naturally, financial analysts and regulators are interested in mitigating sampling errors, as prescribed in EU Regulation 2018/959. In particular, the sampling error of the 99.9% quantile is of paramount importance, along the lines of EU Regulation 575/2013. The Monte Carlo error for the operational risk measure is here assessed on the basis of the binomial distribution. Our approach is then applied to realistic simulated data, yielding a comparable precision of the estimate with a much lower computational effort, when compared to bootstrap, Monte Carlo repetition, and two other methods based on numerical optimization.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:2:p:50-:d:227534
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    References listed on IDEAS

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    3. Cortés, Lina M. & Mora-Valencia, Andrés & Perote, Javier, 2017. "Measuring firm size distribution with semi-nonparametric densities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 485(C), pages 35-47.
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    5. Paul Embrechts & Marco Frei, 2009. "Panjer recursion versus FFT for compound distributions," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 69(3), pages 497-508, July.
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    Cited by:

    1. Teodora Maria SUCIU (AVRAM), 2020. "Possibilities Of Evaluation Of The Expenditure Of The Clothing Industry By The Monte Carlo Method," Contemporary Economy Journal, Constantin Brancoveanu University, vol. 5(3), pages 29-37.
    2. Jiandong Ren & Kristina Sendova & Ričardas Zitikis, 2019. "Special Issue “Risk, Ruin and Survival: Decision Making in Insurance and Finance”," Risks, MDPI, vol. 7(3), pages 1-7, September.

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