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Fundamental Aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk

Book Description

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.

    Table of Contents

    1. Cover
    2. Title page
    3. Copyright page
    4. Dedication page
    5. Preface
    6. Acronyms
    7. List of Distributions
    8. Chapter One: OpRisk in Perspective
      1. 1.1 Brief History
      2. 1.2 Risk-Based Capital Ratios for Banks
      3. 1.3 The Basic Indicator and Standardized Approaches for OpRisk
      4. 1.4 The Advanced Measurement Approach
      5. 1.5 General Remarks and Book Structure
    9. Chapter Two: OpRisk Data and Governance
      1. 2.1 Introduction
      2. 2.2 OpRisk Taxonomy
      3. 2.3 The Elements of the OpRisk Framework
      4. 2.4 Business Environment and Internal Control Environment Factors (BEICFs)
      5. 2.5 External Databases
      6. 2.6 Scenario Analysis
      7. 2.7 OpRisk Profile in Different Financial Sectors
      8. 2.8 Risk Organization and Governance
    10. Chapter Three: Using OpRisk Data for Business Analysis
      1. 3.1 Cost Reduction Programs in Financial Firms
      2. 3.2 Using OpRisk Data to Perform Business Analysis
      3. 3.3 Conclusions
    11. Chapter Four: Stress-Testing OpRisk Capital and the Comprehensive Capital Analysis and Review (CCAR)
      1. 4.1 The Need for Stressing OpRisk Capital Even Beyond 99.9%
      2. 4.2 Comprehensive Capital Review and Analysis (CCAR)
      3. 4.3 OpRisk and Stress Tests
      4. 4.4 OpRisk in CCAR in Practice
      5. 4.5 Reverse Stress Test
      6. 4.6 Stressing OpRisk Multivariate Models—Understanding the Relationship Among Internal Control Factors and Their Impact on Operational Losses
    12. Chapter Five: Basic Probability Concepts in Loss Distribution Approach
      1. 5.1 Loss Distribution Approach
      2. 5.2 Quantiles and Moments
      3. 5.3 Frequency Distributions
      4. 5.4 Severity Distributions
      5. 5.5 Convolutions and Characteristic Functions
      6. 5.6 Extreme Value Theory
    13. Chapter Six: Risk Measures and Capital Allocation
      1. 6.1 Development of Capital Accords Base I, II and III
      2. 6.2 Measures of Risk
      3. 6.3 Capital Allocation
    14. Chapter Seven: Estimation of Frequency and Severity Models
      1. 7.1 Frequentist Estimation
      2. 7.2 Bayesian Inference Approach
      3. 7.3 Mean Square Error of Prediction
      4. 7.4 Standard Markov Chain Monte Carlo (MCMC) Methods
      5. 7.5 Standard MCMC Guidelines for Implementation
      6. 7.6 Advanced MCMC Methods
      7. 7.7 Sequential Monte Carlo (SMC) Samplers and Importance Sampling
      8. 7.8 Approximate Bayesian Computation (ABC) Methods
      9. 7.9 OpRisk Estimation and Modeling for Truncated Data
    15. Chapter Eight: Model Selection and Goodness-of-Fit Testing for Frequency and Severity Models
      1. 8.1 Qualitative Model Diagnostic Tools
      2. 8.2 Tail Diagnostics
      3. 8.3 Information Criterion for Model Selection
      4. 8.4 Goodness-of-Fit Testing for Model Choice (How to Account for Heavy Tails!)
      5. 8.5 Bayesian Model Selection
      6. 8.6 SMC Sampler Estimators of Model Evidence
      7. 8.7 Multiple Risk Dependence Structure Model Selection: Copula Choice
    16. Chapter Nine: Flexible Parametric Severity Models
      1. 9.1 Motivation for Flexible Parametric Severity Loss Models
      2. 9.2 Context of Flexible Heavy-Tailed Loss Models in OpRisk and Insurance LDA Models
      3. 9.3 Empirical Analysis Justifying Heavy-Tailed Loss Models in OpRisk
      4. 9.4 Quantile Function Heavy-Tailed Severity Models
      5. 9.5 Generalized Beta Family of Heavy-Tailed Severity Models
      6. 9.6 Generalized Hyperbolic Families of Heavy-Tailed Severity Models
      7. 9.7 Halphen Family of Flexible Severity Models: GIG and Hyperbolic
    17. Chapter Ten: Dependence Concepts
      1. 10.1 Introduction to Concepts in Dependence for OpRisk and Insurance
      2. 10.2 Dependence Modeling Within and Between LDA Model Structures
      3. 10.3 General Notions of Dependence
      4. 10.4 Dependence Measures
      5. 10.5 Tail Dependence Parameters, Functions, and Tail Order Functions
    18. Chapter Eleven: Dependence Models
      1. 11.1 Introduction to Parametric Dependence Modeling Through a Copula
      2. 11.2 Copula Model Families for OpRisk
      3. 11.3 Copula Parameter Estimation in Two Stages: Inference for the Margins
    19. Chapter Twelve: Examples of LDA Dependence Models
      1. 12.1 Multiple Risk LDA Compound Poisson Processes and Lévy Copula
      2. 12.2 Multiple Risk LDA: Dependence Between Frequencies via Copula
      3. 12.3 Multiple Risk LDA: Dependence Between the k-th Event Times/Losses
      4. 12.4 Multiple Risk LDA: Dependence Between Aggregated Losses via Copula
      5. 12.5 Multiple Risk LDA: Structural Model with Common Factors
      6. 12.6 Multiple Risk LDA: Stochastic and Dependent Risk Profiles
      7. 12.7 Multiple Risk LDA: Dependence and Combining Different Data Sources
      8. 12.8 A Note on Negative Diversification and Dependence Modeling
    20. Chapter Thirteen: Loss Aggregation
      1. 13.1 Analytic Solution
      2. 13.2 Monte Carlo Method
      3. 13.3 Panjer Recursion
      4. 13.4 Panjer Extensions
      5. 13.5 Fast Fourier Transform
      6. 13.6 Closed-Form Approximation
      7. 13.7 Capital Charge Under Parameter Uncertainty
      8. 13.8 Special Advanced Topics on Loss Aggregation
    21. Chapter Fourteen: Scenario Analysis
      1. 14.1 Introduction
      2. 14.2 Examples of Expert Judgments
      3. 14.3 Pure Bayesian Approach (Estimating Prior)
      4. 14.4 Expert Distribution and Scenario Elicitation: Learning from Bayesian Methods
      5. 14.5 Building Models for Elicited Opinions: Hierarchical Dirichlet Models
      6. 14.6 Worst-Case Scenario Framework
      7. 14.7 Stress Test Scenario Analysis
      8. 14.8 Bow-Tie Diagram
      9. 14.9 Bayesian Networks
      10. 14.10 Discussion
    22. Chapter Fifteen: Combining Different Data Sources
      1. 15.1 Minimum Variance Principle
      2. 15.2 Bayesian Method to Combine Two Data Sources
      3. 15.3 Estimation of the Prior Using Data
      4. 15.4 Combining Expert Opinions with External and Internal Data
      5. 15.5 Combining Data Sources Using Credibility Theory
      6. 15.6 Nonparametric Bayesian Approach via Dirichlet Process
      7. 15.7 Combining Using Dempster–Shafer Structures and p-Boxes
      8. 15.8 General Remarks
    23. Chapter Sixteen: Multifactor Modeling and Regression for Loss Processes
      1. 16.1 Generalized Linear Model Regressions and the Exponential Family
      2. 16.2 Maximum Likelihood Estimation for Generalized Linear Models
      3. 16.3 Bayesian Generalized Linear Model Regressions and Regularization Priors
      4. 16.4 Bayesian Estimation and Model Selection via SMC Samplers
      5. 16.5 Illustrations of SMC Samplers Model Estimation and Selection for Bayesian GLM Regressions
      6. 16.6 Introduction to Quantile Regression Methods for OpRisk
      7. 16.7 Factor Modeling for Industry Data
      8. 16.8 Multifactor Modeling under EVT Approach
    24. Chapter Seventeen: Insurance and Risk Transfer
      1. 17.1 Motivation for Insurance and Risk Transfer in OpRisk
      2. 17.2 Fundamentals of Insurance Product Structures for OpRisk
      3. 17.3 Single Peril Policy Products for OpRisk
      4. 17.4 Generic Insurance Product Structures for OpRisk
      5. 17.5 Closed-Form LDA Models with Insurance Mitigations
    25. Chapter Eighteen: Insurance and Risk Transfer
      1. 18.1 Insurance-Linked Securities and CAT Bonds for OpRisk
      2. 18.2 Basics of Valuation of ILS and CAT Bonds for OpRisk
      3. 18.3 Applications of Pricing ILS and CAT Bonds
      4. 18.4 Sidecars, Multiple Peril Baskets, and Umbrellas for OpRisk
      5. 18.5 Optimal Insurance Purchase Strategies for OpRisk Insurance via Multiple Optimal Stopping Times
    26. Appendix A: Miscellaneous Definitions and List of Distributions
      1. A.1 Indicator Function
      2. A.2 Gamma Function
      3. A.3 Discrete Distributions
      4. A.4 Continuous Distributions
    27. Bibliography
    28. Index
    29. Series Page
    30. End User License Agreement