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

Greselin, F., Piacenza, F., Zitikis, R. (2019). Practice Oriented and Monte Carlo Based Estimation of the Value-at-Risk for Operational Risk Measurement. RISKS, 7(2), 1-20 [10.3390/risks7020050].

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

Greselin, F;
2019

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
Articolo in rivista - Articolo scientifico
advanced measurement approach; confidence interval; Monte Carlo; operational risk; value-at-risk
English
1-mag-2019
2019
7
2
1
20
50
open
Greselin, F., Piacenza, F., Zitikis, R. (2019). Practice Oriented and Monte Carlo Based Estimation of the Value-at-Risk for Operational Risk Measurement. RISKS, 7(2), 1-20 [10.3390/risks7020050].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/227990
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