Buch, Englisch, 928 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 1557 g
Reihe: Wiley Handbooks in Financial Engineering and Econometrics
A Handbook of Operational Risk
Buch, Englisch, 928 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 1557 g
Reihe: Wiley Handbooks in Financial Engineering and Econometrics
ISBN: 978-1-118-11839-9
Verlag: Wiley
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.
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Betriebswirtschaft Management Risikomanagement
- Mathematik | Informatik Mathematik Mathematik Interdisziplinär Finanz- und Versicherungsmathematik
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Risikobewertung, Risikotheorie
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
Weitere Infos & Material
Preface xxi
Acronyms xxv
1 OpRisk in Perspective 1
1.1 Brief History 1
1.2 Risk-Based Capital Ratios for Banks 5
1.3 The Basic Indicator and Standardized Approaches for OpRisk 9
1.4 The Advanced Measurement Approach 11
1.5 General Remarks and Book Structure 16
2 OpRisk Data and Governance 17
2.1 Introduction 17
2.2 OpRisk Taxonomy 18
2.3 The Elements of the OpRisk Framework 25
2.4 Business Environment and Internal Control Environment Factors (BEICFs) 29
2.5 External Databases 32
2.6 Scenario Analysis 33
2.7 OpRisk Profile in Different Financial Sectors 36
2.8 Risk Organization and Governance 43
3 Using OpRisk Data for Business Analysis 49
3.1 Cost Reduction Programs at Financial Firms 50
3.2 Using OpRisk Data to Perform Business Analysis 54
3.3 The Risk of Losing Key Talents: OpRisk in Human Resources 55
3.4 Systems Risks: OpRisk in Systems Development and Transaction Processing 56
3.5 Conclusions 59
4 Stress Testing OpRisk Capital and CCAR 61
4.1 The Need for Stressing OpRisk Capital Even Beyond the 99.9% 61
4.2 Comprehensive Capital Review and Analysis (CCAR) 62
4.3 OpRisk and Stress Tests 68
4.4 OpRisk in CCAR in Practice 69
4.5 Reverse Stress Test 75
4.6 Stressing OpRisk Multivariate Models 75
5 Basic Probability Concepts in Loss Distribution Approach 79
5.1 Loss Distribution Approach 79
5.2 Quantiles and Moments 84
5.3 Frequency Distributions 87
5.4 Severity Distributions 88
5.5 Convolutions and Characteristic Functions 93
5.6 Extreme Value Theory 95
6 Risk Measures and Capital Allocation 101
6.1 Development of Capital Accords Base I, II and III 102
6.2 Measures of Risk 105
6.3 Capital Allocation 130
7 Estimation of Frequency and Severity Models 143
7.1 Frequentist Estimation 143
7.2 Bayesian Inference Approach 155
7.3 Mean Square Error of Prediction 160
7.4 Standard Markov Chain Monte Carlo Methods. 161
7.5 Standard MCMC Guidelines for Implementation 174
7.6 Advanced Markov chain Monte Carlo Methods 182
7.7 Sequential Monte Carlo Samplers and Importance Sampling 194
7.8 Approximate Bayesian Computation (ABC) Methods 212
7.9 Modelling Truncated Data 215
8 Model Selection and Goodness of Fit Testing 231
8.1 Qualitative Model Diagnostic Tools 231
8.2 Information Criterion for Model Selection 235
8.3 Goodness of Fit Testing for Model Choice (How to Account for Heavy Tails!) 239
8.4 Bayesian Model Selection 274
8.5 SMC Samplers Estimators of Model Evidence 276
8.6 Multiple Risk Dependence Structure Model Selection: Copula Choice 277
9 Flexible Parametric Severity Models: Basics 289
9.1 Motivation for Flexible Parametric Severity Loss Models 289
9.2 Context of Flexible Heavy Tailed Loss Models in OpRisk and Insurance LDA Models 290
9.3 Empirical Analysis Justifying Heavy Tailed Loss Models in OpRisk 292
9.4 Flexible Distributions for Severity Models in OpRisk 294
9.5 Quantile Function Heavy Tailed Severity Models 294
9.6 Generalized Beta Family of Heavy Tailed Severity Models 321
9.7 Generalized Hyperbolic Families of Heavy Tailed Severity Models 328
9.8 Halphen Family of Flexible Severity Models: GIG and Hyperbolic 338
10 Modelling Dependence 353
10.1 Dependence Modelling Within and Between LDA Model Structures 353
10.2 General Notions of Dependence 358
10.3 Dependence Measures and Tail Dependence 364
10.4 Introduction to Parametric Dependence Modeling Through a Copula 380
10.5 Copula Model Families for OpRisk 387
10.6 Copula Parameter Estimation in Two Stages: Inference For the Margins 416
10.7 Multiple Risk LDA Compound Poisson Processes and Lévy Copula 420
10.8 Multiple Risk LDA: Dependence Between Frequencies via Copula 425
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 430
10.11 Multiple Risk LDA: Structural Model with Common Factors 432
10.12 Multiple Risk LDA: Stochastic and Dependent Risk Profiles 434
10.13 Multiple Risk LDA: Dependence and Combining Different Data Sources437
10.14 A Note on Negative Diversification and Dependence Modelling 445
11 Loss Aggregation 447
11.1 Introduction 447
11.2 Analytic Solution 448
11.3 Monte Carlo Method 454
11.4 Panjer Recursion 457
11.5 Panjer Extensions 462
11.6 Fast Fourier Transform 463
11.7 Closed-Form Approximation 466
11.8 Capital Charge Under Parameter Uncertainty 471
12 Scenario Analysis 477
12.1 Introduction 477
12.2 Examples of Expert Judgements 480
12.3 Pure Bayesian Approach (Estimating Prior) 482
12.4 Expert Distribution and Scenario Elicitation: learning from Bayesian methods 484
12.5 Building Models for Elicited Opinions: Heirarchical Dirichlet Models 487
12.6 Worst Case Scenario Framework 489
12.7 Stress Test Scenario Analysis 492
12.8 Bow-Tie Diagram 495
12.9 Bayesian Networks 497
12.10 Discussion 504
13 Combining Different Data Sources 507
13.1 Minimum variance principle 508
13.2 Bayesian Method to Combine Two Data Sources 510
13.3 Estimation of the Prior Using Data 528
13.4 Combining Expert Opinions with External and Internal Data 530
13.5 Combining Data Sources Using Credibility Theory 546
13.6 Nonparametric Bayesian approach via Dirichlet process 556
13.7 Combining using Dempster-Shafer structures and p-boxes 558
13.8 General Remarks 567
14 Multifactor Modelling and Regression for Loss Processes 571
14.1 Generalized Linear Model Regressions and the Exponential Family 571
14.2 Maximum Likelihood Estimation for Generalized Linear Models 573
14.3 Bayesian Generalized Linear Model Regressions and Regularization Priors 576
14.4 Bayesian Estimation and Model Selection via SMC Samplers 583
14.5 Illustrations of SMC Samplers Model Estimation and Selection for Bayesian GLM Regressions 585
14.6 Introduction to Quantile Regression Methods for OpRisk 590
14.7 Factor Modelling for Industry Data 597
14.8 Multifactor Modelling under EVT Approach 599
15 Insurance and Risk Transfer: Products and Modelling 601
15.1 Motivation for Insurance and Risk Transfer in OpRisk 602
15.2 Fundamentals on Insurance Product Structures for OpRisk 604
15.3 Single Peril Policy Products for OpRisk 609
15.4 Generic Insurance Product Structures for OpRisk 611
15.5 Closed Form LDA Models with Insurance Mitigations 621
16 Insurance and Risk Transfer: Pricing 663
16.1 Insurance Linked Securities and Catastrophe Bonds for OpRisk 664
16.2 Basics of Valuation of Insurance Linked Securities and Catastrophe Bonds for OpRisk 679
16.3 Applications of Pricing Insurance Linked Securities and Catastrophe Bonds 709
16.4 Sidecars, Multiple Peril Baskets and Umbrellas for OpRisk 726
16.5 Optimal Insurance Purchase Strategies for OpRisk Insurance via Multiple Optimal Stopping Times 733
A. Miscellaneous Definitions and List of Distributions 751
A.1 Indicator Function 751
A.2 Gamma Function 751
A.3 Discrete Distributions 752
A.4 Continuous Distributions 753
Index 811
