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 models Methods 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.
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 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 and External Data Underreporting Implementation Programming in R
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