Fundamental Aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk
- 17h 54m
- Gareth W. Peters, Marcelo G. Cruz, Pavel V. Shevchenko
- John Wiley & Sons (US)
- 2015
A one-stop guide for the theories, applications, and statistical methodologies essential to operational risk
Providing a complete overview of operational risk modeling and relevant insurance analytics, Fundamental Aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk offers a systematic approach that covers the wide range of topics in this area. Written by a team of leading experts in the field, the handbook presents detailed coverage of the theories, applications, and models inherent in any discussion of the fundamentals of operational risk, with a primary focus on Basel II/III regulation, modeling dependence, estimation of risk models, and modeling the data elements.
Fundamental Aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk begins with coverage on the four data elements used in operational risk framework as well as processing risk taxonomy. The book then goes further in-depth into the key topics in operational risk measurement and insurance, for example diverse methods to estimate frequency and severity models. Finally, the book ends with sections on specific topics, such as scenario analysis; multifactor modeling; and dependence modeling. A unique companion with Advances in Heavy Tailed Risk Modeling: A Handbook of Operational Risk, the handbook also features:
- Discussions on internal loss data and key risk indicators, which are both fundamental for developing a risk-sensitive framework
- Guidelines for how operational risk can be inserted into a firm’s strategic decisions
- A model for stress tests of operational risk under the United States Comprehensive Capital Analysis and Review (CCAR) program
A valuable reference for financial engineers, quantitative analysts, risk managers, and large-scale consultancy groups advising banks on their internal systems, the handbook is also useful for academics teaching postgraduate courses on the methodology of operational risk.
About the Author
Marcelo G. Cruz, PhD, is Adjunct Professor at New York University and a world-renowned consultant on operational risk modeling and measurement. He has written and edited several books in operational risk, and is Founder and Editor-in-Chief of The Journal of Operational Risk.
Gareth W. Peters, PhD, is Assistant Professor in the Department of Statistical Science, Principle Investigator in Computational Statistics and Machine Learning, and Academic Member of the UK PhD Centre of Financial Computing at University College London. He is also Adjunct Scientist in the Commonwealth Scientific and Industrial Research Organisation, Australia; Associate Member Oxford-Man Institute at the Oxford University; and Associate Member in the Systemic Risk Centre at the London School of Economics.
Pavel V. Shevchenko, PhD, is Senior Principal Research Scientist in the Commonwealth Scientific and Industrial Research Organisation, Australia, as well as Adjunct Professor at the University of New South Wales and the University of Technology, Sydney. He is also Associate Editor of The Journal of Operational Risk. He works on research and consulting projects in the area of financial risk and the development of relevant numerical methods and software, has published extensively in academic journals, consults for major financial institutions, and frequently presents at industry and academic conferences.
In this Book
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Acronyms
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OpRisk in Perspective
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OpRisk Data and Governance
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Using OpRisk Data for Business Analysis
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Stress-Testing OpRisk Capital and the Comprehensive Capital Analysis and Review (CCAR)
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Basic Probability Concepts in Loss Distribution Approach
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Risk Measures and Capital Allocation
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Estimation of Frequency and Severity Models
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Model Selection and Goodness-of-Fit Testing for Frequency and Severity Models
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Flexible Parametric Severity Models—Basics
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Dependence Concepts
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Dependence Models
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Examples of LDA Dependence Models
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Loss Aggregation
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Scenario Analysis
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Combining Different Data Sources
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Multifactor Modeling and Regression for Loss Processes
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Insurance and Risk Transfer—Products and Modeling
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Insurance and Risk Transfer—Pricing Insurance-Linked Derivatives, Reinsurance, and CAT Bonds for OpRisk
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Bibliography
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Color Insert
