Overview
- Shows the advantages of operational risk data analysis
- Introduces an impartial method for identifying the risk classes for operational risk losses
- Uses the R software to implement the proposed procedures
Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (6 chapters)
Keywords
About this book
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.
Authors and Affiliations
About the authors
Giovanni De Luca is a Professor of Economic Statistics and was coordinator of the bachelor degree in Statistics (until November 2019) at Parthenope University, Naples, Italy, where he has taught since 2003. He received his Ph.D. in Mathematical and Statistical Methods from the University of Perugia in 1997. From 1999 to 2002, he worked as an Assistant Professor at the University of Verona. His research interests include time series analysis and statistics for financial markets. Much of his work is focused on the modeling of the dependence structure among variables. He has also investigated mixture models for improving volatility prediction.
Danilo Carità obtained his Ph.D. in Economics, Sustainability and Statistics in 2018. He holds a bachelor’s degree in Statistics and a master’s degree in Quantitative Methods for Economics. He has participated in international conferences and contributed to the Econometric Research in Finance journal.
Francesco Martinelli is a senior financial quantitative analyst manager at UBI Banca. For 20 years, he has worked in the field of quantitative analysis applied to financial markets, in risk management, particularly market risk, credit risk, operational risk and counterparty risk sectors, asset management and the process of validation of internal models. He is also an expert on the estimation of the integrated macro-financial model.
Bibliographic Information
Book Title: Statistical Analysis of Operational Risk Data
Authors: Giovanni De Luca, Danilo Carità , Francesco Martinelli
Series Title: SpringerBriefs in Statistics
DOI: https://doi.org/10.1007/978-3-030-42580-7
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Softcover ISBN: 978-3-030-42579-1Published: 25 February 2020
eBook ISBN: 978-3-030-42580-7Published: 24 February 2020
Series ISSN: 2191-544X
Series E-ISSN: 2191-5458
Edition Number: 1
Number of Pages: IX, 84
Number of Illustrations: 24 b/w illustrations, 44 illustrations in colour
Topics: Statistics for Business, Management, Economics, Finance, Insurance, Risk Management, Economic Theory/Quantitative Economics/Mathematical Methods, Financial Services, Applications of Mathematics