Quantitative operational risk models /

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 rea...

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Bibliographic Details
Corporate Author: Taylor & Francis
Other Authors: Bolancé, Catalina
Format: eBook
Language:English
Published: Boca Raton : Taylor & Francis, 2012.
Series:Chapman & Hall/CRC finance series.
Subjects:
Online Access:Connect to the full text of this electronic book

MARC

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245 0 0 |a Quantitative operational risk models /  |c Catalina Bolancé [and others]. 
260 |a Boca Raton :  |b Taylor & Francis,  |c 2012. 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Chapman & Hall/CRC finance series 
504 |a Includes bibliographical references and index. 
588 0 |a Print version record. 
505 0 |a Understanding Operational Risk; Introduction; Our Approach to Operational Risk Quantification; Regulatory Framework; The Fundamentals of Calculating Operational Risk Capital; Notation and Definitions; The Calculation of Operational Risk Capital in Practice; Organization of the Book; ; Operational Risk Data and Parametric Models; Introduction; Internal Data and External Data; Basic Parametric Severity Distributions; The Generalized Champernowne Distribution; Quantile Estimation; Further Reading and Bibliographic Notes; ; Semiparametric Model for Operational Risk Severities; Introduction; Classical Kernel Density Estimation; Transformation Method; Bandwidth Selection; Boundary Correction; Transformation with the Generalized Champernowne Distributions; Results for the Operational Risk Data; Further Reading and Bibliographic Notes; ; Combining Operational Risk. 
505 0 |a Data Sources; Why Mixing?; Combining Data Sources with the Transformation Method; The Mixing Transformation Technique; ; Data Study; Further Reading and Bibliographic Notes; ; Underreporting; Introduction; The Underreporting Function; Publicly Reported Loss Data; Semiparametric Approach to Correction tor Underreporting; An Application to Evaluate Operational Risk with Correction; An Application to Evaluate Internal Operational Risk; Further Reading and Bibliographic Notes; ; Combining Underreported Internal and External Data; Introduction; Data Availability; Underreporting Losses; A Mixing Model in a Truncation Framework; Operational Risk Application; Further Reading and Bibliographic Notes; ; A Guided Practical Example; Introduction; Descriptive Statistics and Basic Procedures; Transformation Kernel Estimation; Combining Internal. 
505 0 |a And External Data; Underreporting Implementation; Programming in R. 
520 |a 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. 
650 0 |a Risk management. 
650 0 |a Operational risk. 
650 0 |a Mathematical models. 
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830 0 |a Chapman & Hall/CRC finance series. 
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