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Statistical analysis of operational risk data

This concise book for practitioners presents the statistical analysis of operational risk, which is considered the most relevant source of bank risk, after market and credit risk. The book shows that a careful statistical analysis can improve the results of the popular loss distribution approach. Th...

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Detalles Bibliográficos
Autores principales: De Luca, Giovanni, Carità, Danilo, Martinelli, Francesco
Lenguaje:eng
Publicado: Springer 2020
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-030-42580-7
http://cds.cern.ch/record/2711894
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author De Luca, Giovanni
Carità, Danilo
Martinelli, Francesco
author_facet De Luca, Giovanni
Carità, Danilo
Martinelli, Francesco
author_sort De Luca, Giovanni
collection CERN
description This concise book for practitioners presents the statistical analysis of operational risk, which is considered the most relevant source of bank risk, after market and credit risk. The book shows that a careful statistical analysis can improve the results of the popular loss distribution approach. The authors identify the risk classes by applying a pooling rule based on statistical tests of goodness-of-fit, use the theory of the mixture of distributions to analyze the loss severities, and apply copula functions for risk class aggregation. Lastly, they assess operational risk data in order to estimate the so-called capital-at-risk that represents the minimum capital requirement that a bank has to hold. The book is primarily intended for quantitative analysts and risk managers, but also appeals to graduate students and researchers interested in bank risks.
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spelling cern-27118942021-04-21T18:09:24Zdoi:10.1007/978-3-030-42580-7http://cds.cern.ch/record/2711894engDe Luca, GiovanniCarità, DaniloMartinelli, FrancescoStatistical analysis of operational risk dataMathematical Physics and MathematicsThis concise book for practitioners presents the statistical analysis of operational risk, which is considered the most relevant source of bank risk, after market and credit risk. The book shows that a careful statistical analysis can improve the results of the popular loss distribution approach. The authors identify the risk classes by applying a pooling rule based on statistical tests of goodness-of-fit, use the theory of the mixture of distributions to analyze the loss severities, and apply copula functions for risk class aggregation. Lastly, they assess operational risk data in order to estimate the so-called capital-at-risk that represents the minimum capital requirement that a bank has to hold. The book is primarily intended for quantitative analysts and risk managers, but also appeals to graduate students and researchers interested in bank risks.Springeroai:cds.cern.ch:27118942020
spellingShingle Mathematical Physics and Mathematics
De Luca, Giovanni
Carità, Danilo
Martinelli, Francesco
Statistical analysis of operational risk data
title Statistical analysis of operational risk data
title_full Statistical analysis of operational risk data
title_fullStr Statistical analysis of operational risk data
title_full_unstemmed Statistical analysis of operational risk data
title_short Statistical analysis of operational risk data
title_sort statistical analysis of operational risk data
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-030-42580-7
http://cds.cern.ch/record/2711894
work_keys_str_mv AT delucagiovanni statisticalanalysisofoperationalriskdata
AT caritadanilo statisticalanalysisofoperationalriskdata
AT martinellifrancesco statisticalanalysisofoperationalriskdata