Quantitative Operational Risk Models

AvCatalina Bolancé,Montserrat Guillén

Häftad, Engelska, 2023

Del i serien Chapman & Hall/CRC Finance Series

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Beskrivning

Using real-life examples from the banking and insurance industries, Quantitative Operational Risk Models details how internal data can be improved based on external information of various kinds. Using a simple and intuitive methodology based on classical transformation methods, the book includes real-life examples of the combination of internal data and external information.A guideline for practitioners, the book begins with the basics of managing operational risk data to more sophisticated and recent tools needed to quantify the capital requirements imposed by operational risk. The book then covers statistical theory prerequisites, and explains how to implement the new density estimation methods for analyzing the loss distribution in operational risk for banks and insurance companies. In addition, it provides: Simple, intuitive, and general methods to improve on internal operational risk assessment Univariate event loss severity distributions analyzed using semiparametric modelsMethods for the introduction of underreporting information A practical method to combine internal and external operational risk data, including guided examples in SAS and R Measuring operational risk requires the knowledge of the quantitative tools and the comprehension of insurance activities in a very broad sense, both technical and commercial. Presenting a nonparametric approach to modeling operational risk data, Quantitative Operational Risk Models offers a practical perspective that combines statistical analysis and management orientations.

Produktinformation

Utgivningsdatum:2023-03-29
Mått:156 x 234 x 13 mm
Vikt:240 g
Format:Häftad
Språk:Engelska
Serie:Chapman & Hall/CRC Finance Series
Antal sidor:236
Förlag:Taylor & Francis Ltd
ISBN:9781032477572

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Mer om författaren

Catalina Bolance has been Associate Professor of Quantitative Methods for Economics and Management Science of the Department of Econometrics at University of Barcelona since 2001. She received a PhD in Economics, an M.A. in Marketing, and a B.Sc. in Statistics at the University of Barcelona. She is currently member of the research group Risk in Finance and Insurance and is a specialist in applied nonparametric methods. She has coauthored several undergraduate books on applied statistics that are widely used in Spanish universities and has also published in high-quality scientific journals like Insurance: Mathematics and Economics, Statistics, and Astin Bulletin-The Journal of the International Actuarial Association, the journal of the International Actuarial Association. She has supervised many Master's and Ph.D. theses with an outstanding tutoring record. Since 2010 she has participated in a project of the London School of Economics on long-term care insurance sponsored by the AXA research fund. In 2004 she received the insurance international prize awarded by MAPFRE.Montserrat Guillen has been Chair Professor of Quantitative Methods at the University of Barcelona since 2001 and director of the research group on Risk in Finance and Insurance. She received an M.S. degree in Mathematics and Mathematical Statistics in 1987, and a Ph.D. degree in Economics from the University of Barcelona in 1992. She also received theM.A. degree in Data Analysis from the University of Essex, United Kingdom. She was Visiting Research faculty at the University of Texas at Austin (USA) in 1994. She holds a visiting professor position at the University of Paris II, where she teaches Insurance Econometrics. Her research focuses on actuarial statistics and quantitative risk management. Since 2005 she has been an associate editor for the Journal of Risk and Insurance, the official journal of the American Risk an

Recensioner i media

"… a very useful addition to the literature on Operational Financial Risk and I would recommend it to practitioners."—Alan Penman, Annals of Actuarial Science, Vol. 7, March 2013

Innehållsförteckning

Understanding Operational Risk. Operational Risk Data and Parametric Models. Semiparametric Model for Operational Risk Severities. Combining Operational Risk Data Sources. Data Study. Underreporting. Combining Underreported Internal and External Data. A Guided Practical Example.