Operational risk, operational risk measurement, regulatory and legal issues, Basel II definition, elementary approaches, basic indicator approach, advanced measurement approaches, severity distribution, frequency distribution, operational loss data, reporting bias, insurance analytics, actuarial methodology, total loss amount, Normal approximation, translated-gamma approximation, Panjer recursion, Poisson mixtures, tails of aggregate loss distributions, homogeneous Poisson process
From the Authors - In the first half of this chapter (Section 10.1) we examine the Basel II requirements for the quantitative modelling of operational risk, discussing various potential approaches. On the basis of some industry data we highlight the possibilities and limitations of existing tools for the calculation of an operational risk-capital charge. In Section 10.2 we summarize the techniques from actuarial modelling that are relevant to operational risk, under the heading of insurance analytics. Our discussion in that section, though motivated by quantitative modelling of operational risk, has a much wider applicability in quantitative risk management. For example, some techniques have implicitly been used in the credit risk chapters. The Notes and Comments section at the end of the chapter gives an overview of further techniques from insurance mathematics that we feel will become useful in the years to come.
10.1 Operational Risk in Perspective
10.1.1 A New Risk Class
10.1.2 The Elementary Approaches
10.1.3 Advanced Measurement Approaches
10.1.4 Operational Loss Data
10.2 Elements of Insurance Analytics
10.2.1 The Case for Actuarial Methodology
10.2.2 The Total Loss Amount
10.2.3 Approximations and Panjer Recursion
10.2.4 Poisson Mixtures
10.2.5 Tails of Aggregate Loss Distributions
10.2.6 The Homogeneous Poisson Process
10.2.7 Processes Related to the Poisson Process
*** From the publisher ***
The implementation of sound quantitative risk models is a vital concern for all financial institutions, and this trend has accelerated in recent years with regulatory processes such as Basel II. This book provides a comprehensive treatment of the theoretical concepts and modelling techniques of quantitative risk management and equips readers--whether financial risk analysts, actuaries, regulators, or students of quantitative finance--with practical tools to solve real-world problems. The authors cover methods for market, credit, and operational risk modelling; place standard industry approaches on a more formal footing; and describe recent developments that go beyond, and address main deficiencies of, current practice.
The book`s methodology draws on diverse quantitative disciplines, from mathematical finance through statistics and econometrics to actuarial mathematics. Main concepts discussed include loss distributions, risk measures, and risk aggregation and allocation principles. A main theme is the need to satisfactorily address extreme outcomes and the dependence of key risk drivers. The techniques required derive from multivariate statistical analysis, financial time series modelling, copulas, and extreme value theory. A more technical chapter addresses credit derivatives. Based on courses taught to masters students and professionals, this book is a unique and fundamental reference that is set to become a standard in the field.