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Statistical Analysis of Operational Risk Data

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Statistical Analysis of Operational Risk Data / by Giovanni De Luca, Danilo Carità, Francesco Martinelli.
Author:	De Luca, Giovanni.
other author:	Carità, Danilo.
Description:	IX, 84 p. 68 illus., 44 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Statistics . -
Online resource:	https://doi.org/10.1007/978-3-030-42580-7
ISBN:	9783030425807

Statistical Analysis of Operational Risk Data
De Luca, Giovanni.

Statistical Analysis of Operational Risk Data [electronic resource] /by Giovanni De Luca, Danilo Carità, Francesco Martinelli. - 1st ed. 2020. - IX, 84 p. 68 illus., 44 illus. in color.online resource. - SpringerBriefs in Statistics,2191-544X. - SpringerBriefs in Statistics,0.

1 The Operational Risk -- 2 Identification of the Risk Classes -- 3 Severity Analysis -- 4 Frequency Analysis -- 5 Convolution and Risk Class Aggregation -- 6 Conclusions.

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.

ISBN: 9783030425807

Standard No.: 10.1007/978-3-030-42580-7doiSubjects--Topical Terms:

1253516
Statistics .

LC Class. No.: QA276-280

Dewey Class. No.: 330.015195

Statistical Analysis of Operational Risk Data
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