Statistical Analysis of Operational Risk Data

Author:   Giovanni De Luca ,  Danilo Carità ,  Francesco Martinelli
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
ISBN:  

9783030425791


Pages:   84
Publication Date:   25 February 2020
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Statistical Analysis of Operational Risk Data


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Overview

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.

Full Product Details

Author:   Giovanni De Luca ,  Danilo Carità ,  Francesco Martinelli
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Weight:   0.454kg
ISBN:  

9783030425791


ISBN 10:   3030425797
Pages:   84
Publication Date:   25 February 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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

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Author Information

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.

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