Fundamental aspects of operational risk and insurance analytics : a handbook of operational risk / Marcelo G. Cruz, GLeonard N. Stern School of Business, New York University, New York, NY, USA, Gareth W. Peters, Department of Statistical Science, University College of London, London, United Kingdom, Pavel V. Shevchenko, Division of Computational Informatics, The Commonwealth Scientific and Industrial Research Organization, Sydney, Australia.

Format:

Book

Author/Creator:

Cruz, Marcelo G.

Contributor:

Peters, Gareth W., 1978-

Shevchenko, Pavel V.

Language:

English

Subjects (All):

Operational risk.

Risk management.

Physical Description:

899 pages : 25 cm

Place of Publication:

Hoboken, New Jersey : John Wiley & Sons, Inc., [2014]

Summary:

"Co-edited by acknowledged experts in the quantification of operational risk, Handbook of Operational Risk conveniently and systematically displays all of the financial engineering topics, theories, applications, and current statistical methodologies that are intrinsic to the subject matter. This one-stop guide for financial engineers, quantitative analysts, and risk managers places under one cover all of the necessary theory, applications, and models that are inherent in any discussion of the subject. The authors emphasize the importance of collecting high-quality data based upon understanding the problems that impede the gathering process"-- Provided by publisher.

"Systematically displays all of the financial engineering topics, theories, applications, and current statistical methodologies that are intrinsic to the quantification of operational risk"-- Provided by publisher.

Notes:

Includes bibliographical references and index.

Machine generated contents note: Preface xxi -- Acronyms xxv -- 1 OpRisk in Perspective -- 1.1 Brief History -- 1.2 Risk-Based Capital Ratios for Banks -- 1.3 The Basic Indicator and Standardized Approaches for OpRisk -- 1.4 The Advanced Measurement Approach -- 1.5 General Remarks and Book Structure -- 2 OpRisk Data and Governance -- 2.1 Introduction -- 2.2 OpRisk Taxonomy -- 2.3 The Elements of the OpRisk Framework -- 2.4 Business Environment and Internal Control Environment Factors (BEICFs) -- 2.5 External Databases -- 2.6 Scenario Analysis -- 2.7 OpRisk Profile in Different Financial Sectors -- 2.8 Risk Organization and Governance -- 3 Using OpRisk Data for Business Analysis -- 3.1 Cost Reduction Programs at Financial Firms -- 3.2 Using OpRisk Data to Perform Business Analysis -- 3.3 The Risk of Losing Key Talents: OpRisk in Human Resources -- 3.4 Systems Risks: OpRisk in Systems Development and Transaction Processing -- 3.5 Conclusions -- 4 Stress Testing OpRisk Capital and CCAR -- 4.1 The Need for Stressing OpRisk Capital Even Beyond the 99.9% -- 4.2 Comprehensive Capital Review and Analysis (CCAR) -- 4.3 OpRisk and Stress Tests -- 4.4 OpRisk in CCAR in Practice -- 4.5 Reverse Stress Test -- 4.6 Stressing OpRisk Multivariate Models -- 5 Basic Probability Concepts in Loss Distribution Approach -- 5.1 Loss Distribution Approach -- 5.2 Quantiles and Moments -- 5.3 Frequency Distributions -- 5.4 Severity Distributions -- 5.5 Convolutions and Characteristic Functions -- 5.6 Extreme Value Theory -- 6 Risk Measures and Capital Allocation -- 6.1 Development of Capital Accords Base I, II and III -- 6.2 Measures of Risk -- 6.3 Capital Allocation -- 7 Estimation of Frequency and Severity Models -- 7.1 Frequentist Estimation -- 7.2 Bayesian Inference Approach -- 7.3 Mean Square Error of Prediction -- 7.4 Standard Markov Chain Monte Carlo Methods. -- 7.5 Standard MCMC Guidelines for Implementation -- 7.6 Advanced Markov chain Monte Carlo Methods -- 7.7 Sequential Monte Carlo Samplers and Importance Sampling -- 7.8 Approximate Bayesian Computation (ABC) Methods -- 7.9 Modelling Truncated Data -- 8 Model Selection and Goodness of Fit Testing -- 8.1 Qualitative Model Diagnostic Tools -- 8.2 Information Criterion for Model Selection -- 8.3 Goodness of Fit Testing for Model Choice (How to Account for Heavy Tails!) -- 8.4 Bayesian Model Selection -- 8.5 SMC Samplers Estimators of Model Evidence -- 8.6 Multiple Risk Dependence Structure Model Selection: Copula Choice -- 9 Flexible Parametric Severity Models: Basics -- 9.1 Motivation for Flexible Parametric Severity Loss Models -- 9.2 Context of Flexible Heavy Tailed Loss Models in OpRisk and Insurance LDA Models -- 9.3 Empirical Analysis Justifying Heavy Tailed Loss Models in OpRisk -- 9.4 Flexible Distributions for Severity Models in OpRisk -- 9.5 Quantile Function Heavy Tailed Severity Models -- 9.6 Generalized Beta Family of Heavy Tailed Severity Models -- 9.7 Generalized Hyperbolic Families of Heavy Tailed Severity Models -- 9.8 Halphen Family of Flexible Severity Models: GIG and Hyperbolic -- 10 Modelling Dependence -- 10.1 Dependence Modelling Within and Between LDA Model Structures -- 10.2 General Notions of Dependence -- 10.3 Dependence Measures and Tail Dependence -- 10.4 Introduction to Parametric Dependence Modeling Through a Copula -- 10.5 Copula Model Families for OpRisk -- 10.6 Copula Parameter Estimation in Two Stages: Inference For the Margins -- 10.7 Multiple Risk LDA Compound Poisson Processes and Levy Copula -- 10.8 Multiple Risk LDA: Dependence Between Frequencies via Copula -- 10.9 Multiple Risk LDA: Dependence Between the k-th Event Times/Losses 425 10.10 Multiple Risk LDA: Dependence Between Aggregated Losses via Copula -- 10.11 Multiple Risk LDA: Structural Model with Common Factors -- 10.12 Multiple Risk LDA: Stochastic and Dependent Risk Profiles -- 10.13 Multiple Risk LDA: Dependence and Combining Different Data Sources -- 10.14 A Note on Negative Diversification and Dependence Modelling -- 11 Loss Aggregation -- 11.1 Introduction -- 11.2 Analytic Solution -- 11.3 Monte Carlo Method -- 11.4 Panjer Recursion -- 11.5 Panjer Extensions 462 11.6 Fast Fourier Transform -- 11.7 Closed-Form Approximation -- 11.8 Capital Charge Under Parameter Uncertainty -- 12 Scenario Analysis -- 12.1 Introduction -- 12.2 Examples of Expert Judgements -- 12.3 Pure Bayesian Approach (Estimating Prior) -- 12.4 Expert Distribution and Scenario Elicitation: learning from Bayesian methods -- 12.5 Building Models for Elicited Opinions: Heirarchical Dirichlet Models -- 12.6 Worst Case Scenario Framework -- 12.7 Stress Test Scenario Analysis -- 12.8 Bow-Tie Diagram -- 12.9 Bayesian Networks -- 12.10 Discussion -- 13 Combining Different Data Sources -- 13.1 Minimum variance principle -- 13.2 Bayesian Method to Combine Two Data Sources -- 13.3 Estimation of the Prior Using Data -- 13.4 Combining Expert Opinions with External and Internal Data -- 13.5 Combining Data Sources Using Credibility Theory -- 13.6 Nonparametric Bayesian approach via Dirichlet process -- 13.7 Combining using Dempster-Shafer structures and p-boxes -- 13.8 General Remarks -- 14 Multifactor Modelling and Regression for Loss Processes -- 14.1 Generalized Linear Model Regressions and the Exponential Family -- 14.2 Maximum Likelihood Estimation for Generalized Linear Models -- 14.3 Bayesian Generalized Linear Model Regressions and Regularization Priors -- 14.4 Bayesian Estimation and Model Selection via SMC Samplers -- 14.5 Illustrations of SMC Samplers Model Estimation and Selection for Bayesian GLM Regressions -- 14.6 Introduction to Quantile Regression Methods for OpRisk -- 14.7 Factor Modelling for Industry Data -- 14.8 Multifactor Modelling under EVT Approach -- 15 Insurance and Risk Transfer: Products and Modelling -- 15.1 Motivation for Insurance and Risk Transfer in OpRisk -- 15.2 Fundamentals on Insurance Product Structures for OpRisk -- 15.3 Single Peril Policy Products for OpRisk -- 15.4 Generic Insurance Product Structures for OpRisk -- 15.5 Closed Form LDA Models with Insurance Mitigations -- 16 Insurance and Risk Transfer: Pricing -- 16.1 Insurance Linked Securities and Catastrophe Bonds for OpRisk -- 16.2 Basics of Valuation of Insurance Linked Securities and Catastrophe Bonds for OpRisk -- 16.3 Applications of Pricing Insurance Linked Securities and Catastrophe Bonds -- 16.4 Sidecars, Multiple Peril Baskets and Umbrellas for OpRisk -- 16.5 Optimal Insurance Purchase Strategies for OpRisk Insurance via Multiple Optimal Stopping Times -- A. Miscellaneous Definitions and List of Distributions -- A.1 Indicator Function -- A.2 Gamma Function -- A.3 Discrete Distributions -- A.4 Continuous Distributions -- Index.

Other Format:

Online version: Cruz, Marcelo G. Fundamental aspects of operational risk and insurance analytics

ISBN:

9781118118399

1118118391

OCLC:

877843765

Online:

Cover image