Quantitative risk management: concepts, techniques and tools
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Princeton, NJ [u.a.]
Princeton Univ. Press
2015
|
| Ausgabe: | Rev. ed. |
| Schriftenreihe: | Princeton series in finance
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Im Vorwort als "Second edition" bezeichnet |
| Beschreibung: | XIX, 699 S. graph. Darst. |
| ISBN: | 9780691166278 |
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| 250 | |a Rev. ed. | ||
| 264 | 1 | |a Princeton, NJ [u.a.] |b Princeton Univ. Press |c 2015 | |
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents
Preface XV
I An Introduction to Quantitative Risk Management 1
1 Risk in Perspective 3
1.1 Risk 3
1.1.1 Risk and Randomness 3
LI.2 Financial Risk 5
1.1.3 Measurement and Management 6
1.2 A Brief History of Risk Management 8
1.2.1 From Babylon to Wall Street 8
1.2.2 The Road to Regulation 15
1.3 The Regulatory Framework 20
1.3.1 The Basel Framework 20
1.3.2 The Solvency II Framework 25
1.3.3 Criticism of Regulatory Frameworks 28
1.4 Why Manage Financial Risk? 30
1.4.1 A Societal View 30
1.4.2 The Shareholder’s View 32
1.5 Quantitative Risk Management 34
1.5.1 The Q in QRM 34
1.5.2 The Nature of the Challenge 35
1.5.3 QRM Beyond Finance 38
2 Basic Concepts in Risk Management 42
2.1 Risk Management for a Financial Firm 42
2.1.1 Assets, Liabilities and the Balance Sheet 42
2.1.2 Risks Faced by a Financial Firm 44
2.1.3 Capital 45
2.2 Modelling Value and Value Change 47
2.2.1 Mapping Risks 47
2.2.2 Valuation Methods 54
2.2.3 Loss Distributions 58
2.3 Risk Measurement 61
2.3.1 Approaches to Risk Measurement 61
2.3.2 Value-at-Risk 64
2.3.3 VaR in Risk Capital Calculations 67
viii Contents
2.3.4 Other Risk Measures Based on Loss Distributions 69
2.3.5 Coherent and Convex Risk Measures 72
3 Empirica] Properties of Financial Data 79
3.1 Stylized Facts of Financial Return Series 79
3.1.1 Volatility Clustering 80
3.1.2 Non ֊normality and Heavy Tails 85
3.1.3 Longer-IntervaJ Return Series 87
3.2 Multivariate Stylized Facts 88
3.2.1 Correlation between Series 88
3.2.2 Tail Dependence 90
II Methodology 95
4 Financial Time Series 97
4.1 Fundamentals of Time Series Analysis 98
4.1.1 Basic Definitions 98
4.1.2 ARMA Processes 100
4.1.3 Analysis in the Time Domain 105
4.1,4 Statistical Analysis of Time Series 107
4.1.5 Prediction 109
4.2 G ARCH Models for Changing Volatility 112
4.2.1 ARCH Processes 112
4.2.2 GARCH Processes 118
4.2.3 Simple Extensions of the GARCH Model 121
4.2.4 Fitting GARCH Models to Data 123
4.2.5 Volatility Forecasting and Risk Measure Estimation 129
5 Extreme Value Theory 135
5.1 Maxima 135
5.1.1 Generalized Extreme Value Distribution 136
5.1.2 Maximum Domains of Attraction 139
5.1.3 Maxima of Strictly Stationary Time Series 141
5.1.4 The Block Maxima Method 142
5.2 Threshold Exceedances 146
5.2.1 Generalized Pareto Distribution 147
5.2.2 Modelling Excess Losses 149
5.2.3 Modelling Tails and Measures of Tail Risk 154
5.2.4 The Hill Method 157
5.2.5 Simulation Study of EVT Quantile Estimators 161
5.2.6 Conditional EVT for Financial Time Series 162
5.3 Point Process Models 164
5.3.1 Threshold Exceedances for Strict White Noise 164
5.3.2 The POT Model 166
6 Multivariate Models 173
6.1 Basics of Multivariate Modelling 174
6.1.1 Random Vectors and Their Distributions 174
6.1.2 Standard Estimators of Covanance and Correlation 176
6.1.3 The Multivariate Normal Distribution 178
6.1.4 Testing Multivariate Normality 180
Contents ix
6.2 Normal Mixture Distributions 183
6.2.1 Normal Variance Mixtures 183
6.2.2 Normal Mean ֊Variance Mixtures 187
6.2.3 Generalized Hyperbolic Distributions 188
6.2.4 Empirical Examples 191
6.3 Spherical and Elliptical Distributions 196
6.3.1 Spherical Distributions 196
6.3.2 Elliptical Distributions 200
6.3.3 Properties of Elliptical Distributions 202
6.3.4 Estimating Dispersion and Correlation 203
6.4 Dimension-Reduction Techniques 206
6.4.1 Factor Models 206
6.4.2 Statistical Estimation Strategies 208
6.4.3 Estimating Macroeconomic Factor Models 210
6.4.4 Estimating Fundamental Factor Models 213
6.4.5 Principal Component Analysis 214
7 Copulas and Dependence 220
7.1 Copulas 220
7.1.1 Basic Properties 221
7.1.2 Examples of Copulas 225
7.1.3 Meta Distributions 229
7.1.4 Simulation of Copulas and Meta Distributions 229
7.1.5 Further Properties of Copulas 232
7.2 Dependence Concepts and Measures 235
7.2.1 Perfect Dependence 236
7.2.2 Linear Correlation 238
7.2.3 Rank Correlation 243
7.2.4 Coefficients of Tail Dependence 247
7.3 Normal Mixture Copulas 249
7.3.1 Tail Dependence 249
7.3.2 Rank Correlations 253
7.3.3 Skewed Normal Mixture Copulas 256
7.3.4 Grouped Normal Mixture Copulas 257
7.4 Archimedean Copulas 259
7.4.1 Bivariate Archimedean Copulas 259
7.4.2 Multivariate Archimedean Copulas 261
7.5 Fitting Copulas to Data 265
7.5.1 Method-of-Moments Using Rank Correlation 266
7.5.2 Forming a Pseudo֊sample from the Copula 269
7.5.3 Maximum Likelihood Estimation 270
8 Aggregate Risk 275
8.1 Coherent and Convex Risk Measures 275
8.1.1 Risk Measures and Acceptance Sets 276
8.1.2 Dual Representation of Convex Measures of Risk 280
8.1.3 Examples of Dual Representations 283
8.2 Law-Invariant Coherent Risk Measures 286
8.2.1 Distortion Risk Measures 286
8.2.2 The Bxpectile Risk Measure 290
x
Contents
8.3 Risk Measures for Linear Portfolios 293
8.3.1 Coherent Risk Measures as Stress Tests 293
8.3.2 Elliptically Distributed Risk Factors 295
8.3.3 Other Risk Factor Distributions 297
8.4 Risk Aggregation 299
8.4.1 Aggregation Based on Loss Distributions 300
8.4.2 Aggregation Based on Stressing Risk Factors 302
8.4.3 Modular versus Fully Integrated Aggregation Approaches 303
8.4.4 Risk Aggregation and Freehet Problems 305
8.5 Capital Allocation 315
8.5.1 The Allocation Problem. 315
8.5.2 The Euler Principle and Examples 316
8.5.3 Economic Properties of the Euler Principle 320
HI Applications 323
9 Market Risk 325
9.1 Risk Factors and Mapping 325
9.1.1 The Loss Operator 326
9.1.2 Delta and Delta-Gamma Approximations 327
9.1.3 Mapping Bond Portfolios 329
9.1.4 Factor Models for Bond Portfolios 332
9.2 Market Risk Measurement 338
9.2.1 Conditional and Unconditional Loss Distributions 339
9.2.2 Variance-Covariance Method 340
9.2.3 Historical Simulation 342
9.2.4 Dynamic Historical Simulation 343
9.2.5 Monte Carlo 346
9.2.6 Estimating Risk Measures 347
9.2.7 Losses over Several Periods and Scaling 349
9.3 Backtesting 351
9.3.1 Violation-Based Tests for VaR 352
9.3.2 Violation-Based Tests for Expected Shortfall 354
9.3.3 Elicitability and Comparison of Risk Measure Estimates 355
9.3.4 Empirical Comparison of Methods Using Backtesting Concepts 358
9.3.5 Backtesting the Predictive Distribution 363
10 Credit Risk 366
10.1 Credit-Risky Instruments 367
10.1.1 Loans 367
10.1.2 Bonds 368
10.1.3 Derivative Contracts Subject to Counterparty Risk 369
10.1.4 Credit Default Swaps and Related Credit Derivatives 370
10.1.5 PD, LGD and EAD 372
10.2 Measuring Credit Quality 374
10.2.1 Credit Rating Migration 374
10.2.2 Rating Transitions as a Markov Chain 376
10.3 Structural Models of Default 380
10.3.1 The Merton Model 380
10.3.2 Pricing in Merton’s Model 381
10.3.3 Structural Models in Practice: EDF and DD 386
10.3.4 Credit-Migration Models Revisited 389
Contents
XI
10.4 Bond and CDS Pricing in Hazard Rate Models 391
10.4.1 Hazard Rate Models 391
10.4.2 Risk-Neutral Pricing Revisited 394
10.4.3 Bond Pricing 399
10.4.4 CDS Pricing 401
10.4.5 P versus Q: Empirical Results 404
10.5 Pricing with Stochastic Hazard Rates 406
10.5.1 Doubly Stochastic Random Times 406
10.5.2 Pricing Form ulas 411
10.5.3 Applications 413
10.6 Affine Models 416
10.6.1 Basic Results 417
10.6.2 The CIR Square-Root Diffusion 418
10.6.3 Extensions 420
11 Portfolio Credit Risk Management 425
11.1 Threshold Models 426
11.1.1 Notation for One-Period Portfolio Models 426
11.1.2 Threshold Models and Copulas 428
11.1.3 Gaussian Threshold Models 430
11.1.4 Models Based on Alternative Copulas 431
11.1.5 Model Risk Issues 433
11.2 Mixture Models 436
11.2.1 Bernoulli Mixture Models 436
11.2.2 One-Factor Bernoulli Mixture Models 437
11.2.3 Recovery Risk in Mixture Models 440
11.2.4 Threshold Models as Mixture Models 441
11.2.5 Poisson Mixture Models and CreditRisk+ 444
11.3 Asymptotics for Large Portfolios 449
11.3.1 Exchangeable Models 450
11.3.2 General Results 452
11.3.3 The Basel IRB Formula 455
11.4 Monte Carlo Methods 457
11.4.1 Basics of Importance Sampling 457
11 4.2 Application to Bernoulli Mixture Models 460
11.5 Statistical Inference in Portfolio Credit Models 464
11.5.1 Factor Modelling in Industry Threshold Models 465
11.5.2 Estimation of Bernoulli Mixture Models 466
11.5.3 Mixture Models as GLMMs 470
11.5.4 A One-Factor Model with Rating Effect 472
12 Portfolio Credit Derivatives 476
12.1 Credit Portfolio Products 476
12.1.1 Collateralized Debt Obligations 477
12.1.2 Credit Indices and Index Derivatives 481
12.1.3 Basic Pricing Relationships for Index Swaps and CDOs 484
12.2 Copula Models 487
12.2.1 Definition and Properties 487
12.2.2 Examples 489
12.3 Pricing of Index Derivatives in Factor Copula Models 491
12.3.1 Analytics 491
12.3.2 Correlation Skews 494
12.3.3 The Implied Copula Approach 497
xii
13 Operational Risk and Insurance Analytics
13.1 Operati on al Risk in Perspective
13.1.1 A n Important Risk Cl ass
13.1.2 The Elementary Approaches
13.1.3 Advanced Measurement Approaches
13.1.4 Operational Loss Data
13.2 Elements of Insurance Analytics
13.2.1 The Case for Actuarial Methodology
13.2.2 The Total Loss Amount
13.2.3 Approximations and Panjer Recursion
13.2.4 Poisson Mixtures
13.2.5 Tails of Aggregate Loss Distributions
13.2.6 The Homogeneous Poisson Process
13.2.7 Processes Related to the Poisson Process
IV Special Topics
14 Multivariate Time Series
14.1 Fundamentals of Multivariate Time Series
14.1.1 B asic Definitions
14.1.2 Analysis in the Time Domain
14.1.3 Multivariate ARMA Processes
14.2 Multivariate GARCH Processes
14.2.1 General Structure of Models
14.2.2 Models for Conditional Correlation
14.2.3 Models for Conditional Covariance
14.2.4 Fitting Multivariate GARCH Models
14.2.5 Dimension Reduction in MG ARCH
14.2.6 MG ARCH and Conditional Risk Measurement
15 Advanced Topics in Multivariate Modelling
15.1 Normal Mixture and Elliptical Distributions
15.1.1 Estimation of Generalized Hyperbolic Distributions
15.1.2 Testing for Elliptical Symmetry
15.2 Advanced Archimedean Copula Models
15.2.1 Characterization of Archimedean Copulas
15.2.2 Non-exchangeable Archimedean Copulas
16 Advanced Topics in Extreme Value Theory
16.1 Tails of Specific Models
16.1.1 Domain of Attraction of the Fréchet Distributi on
16.1.2 Domain of Attraction of the Gumbel Distribution
16.1.3 Mixture Models
16.2 Self-exciting Models for Extremes
16.2.1 Self-exciting Processes
16.2.2 A Self-exciting POT Model
16.3 Multivariate Maxima
16.3.1 Multivariate Extreme Value Copulas
16.3.2 Copulas for Multivariate Minima
16.3.3 Copula Domains of Attraction
16.3.4 Modellm g Multivariate Block Maxima
Contents
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Contents
xiii
16.4 Multivariate Threshold Exceedances 591
16.4.1 Threshold Models Using EV Copulas 591
16.4.2 Fitting a Multivariate Tail Model 592
16.4.3 Threshold Copulas and Their Limits 594
17 Dynamic Portfolio Credit Risk Models and Counterparty Risk 599
17.1 Dynamic Portfolio Credit Risk Models 599
17.1.1 Why Dynamic Models of Portfolio Credit Risk? 599
17.1.2 Classes of Reduced-Form Models of Portfolio Credit Risk 600
17.2 Counterparty Credit Risk Management 603
17.2.1 Uncollateralized Value Adjustments for a CDS 604
17.2.2 Collateralized Value Adjustments for a CDS 609
17.3 Conditionally Independent Default Times 612
17.3.1 Definition and Mathematical Properties 612
17.3.2 Examples and Applications 618
17.3.3 Credit Value Adjustments 622
17.4 Credit Risk Models with Incomplete Information 625
17.4.1 Credit Risk and Incomplete Information 625
17.4.2 Pure Default Information 628
17.4.3 Additional Information 633
17.4.4 Collateralized Credit Value Adjustments and Contagion Effects 637
Appendix 641
A.l Miscellaneous Definitions and Results 641
A.1.1 Type of Distribution 641
A.1.2 Generalized Inverses and Quantiles 641
A. 1.3 Distributional Transform 643
A. 1.4 Karamata’s Theorem 644
A.l .5 Supporting and Separating Hyperplane Theorems 644
A.2 Probability Distributions 644
A.2.1 Beta 645
A. 2.2 Exponential 645
A.2.3 F 645
A.2.4 Gamma 645
A.2.5 Generalized Inverse Gaussian 646
A.2.6 Inverse Gamma 646
A.2.1 Negative Binomial 646
A.2.8 Pareto 647
A.2.9 Stable 647
A.3 Likelihood Inference 647
A-3.1 Maximum Likelihood Estimators 648
A.3.2 Asymptotic Results: Scalar Parameter 648
A.3.3 Asymptotic Results: Vector of Parameters 649
A.3.4 Wald Test and Confidence Intervals 649
A.3.5 Likelihood Ratio Test and Confidence Intervals 650
A.3.6 Akaike Information Criterion 650
References 652
Index
687
|
| any_adam_object | 1 |
| author | McNeil, Alexander J. 1967- Frey, Rüdiger 1966- Embrechts, Paul 1953- |
| author_GND | (DE-588)131571338 (DE-588)132012367 (DE-588)115254447 |
| author_facet | McNeil, Alexander J. 1967- Frey, Rüdiger 1966- Embrechts, Paul 1953- |
| author_role | aut aut aut |
| author_sort | McNeil, Alexander J. 1967- |
| author_variant | a j m aj ajm r f rf p e pe |
| building | Verbundindex |
| bvnumber | BV042599025 |
| classification_rvk | QH 237 QP 300 QP 710 QP 890 SK 980 |
| classification_tum | WIR 190f MAT 902f WIR 160f MAT 620f |
| ctrlnum | (OCoLC)915347105 (DE-599)BSZ428318312 |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | Rev. ed. |
| format | Book |
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| id | DE-604.BV042599025 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:05:35Z |
| institution | BVB |
| isbn | 9780691166278 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028032131 |
| oclc_num | 915347105 |
| open_access_boolean | |
| owner | DE-634 DE-2070s DE-355 DE-BY-UBR DE-91G DE-BY-TUM DE-1049 DE-523 DE-20 DE-739 DE-83 DE-19 DE-BY-UBM DE-N2 DE-861 DE-473 DE-BY-UBG DE-Aug4 DE-526 DE-384 DE-29T DE-703 |
| owner_facet | DE-634 DE-2070s DE-355 DE-BY-UBR DE-91G DE-BY-TUM DE-1049 DE-523 DE-20 DE-739 DE-83 DE-19 DE-BY-UBM DE-N2 DE-861 DE-473 DE-BY-UBG DE-Aug4 DE-526 DE-384 DE-29T DE-703 |
| physical | XIX, 699 S. graph. Darst. |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Princeton Univ. Press |
| record_format | marc |
| series2 | Princeton series in finance |
| spelling | McNeil, Alexander J. 1967- Verfasser (DE-588)131571338 aut Quantitative risk management concepts, techniques and tools Alexander J. McNeil, Rüdiger Frey, Paul Embrechts Rev. ed. Princeton, NJ [u.a.] Princeton Univ. Press 2015 XIX, 699 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Princeton series in finance Im Vorwort als "Second edition" bezeichnet Risikomanagement (DE-588)4121590-4 gnd rswk-swf Finanzmathematik (DE-588)4017195-4 gnd rswk-swf Mathematische Methode (DE-588)4155620-3 gnd rswk-swf Finanzmanagement (DE-588)4139075-1 gnd rswk-swf Financial Engineering (DE-588)4208404-0 gnd rswk-swf Kreditrisiko (DE-588)4114309-7 gnd rswk-swf Risikomanagement (DE-588)4121590-4 s Financial Engineering (DE-588)4208404-0 s Mathematische Methode (DE-588)4155620-3 s DE-604 Finanzmanagement (DE-588)4139075-1 s Kreditrisiko (DE-588)4114309-7 s Finanzmathematik (DE-588)4017195-4 s Frey, Rüdiger 1966- Verfasser (DE-588)132012367 aut Embrechts, Paul 1953- Verfasser (DE-588)115254447 aut Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028032131&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | McNeil, Alexander J. 1967- Frey, Rüdiger 1966- Embrechts, Paul 1953- Quantitative risk management concepts, techniques and tools Risikomanagement (DE-588)4121590-4 gnd Finanzmathematik (DE-588)4017195-4 gnd Mathematische Methode (DE-588)4155620-3 gnd Finanzmanagement (DE-588)4139075-1 gnd Financial Engineering (DE-588)4208404-0 gnd Kreditrisiko (DE-588)4114309-7 gnd |
| subject_GND | (DE-588)4121590-4 (DE-588)4017195-4 (DE-588)4155620-3 (DE-588)4139075-1 (DE-588)4208404-0 (DE-588)4114309-7 |
| title | Quantitative risk management concepts, techniques and tools |
| title_auth | Quantitative risk management concepts, techniques and tools |
| title_exact_search | Quantitative risk management concepts, techniques and tools |
| title_full | Quantitative risk management concepts, techniques and tools Alexander J. McNeil, Rüdiger Frey, Paul Embrechts |
| title_fullStr | Quantitative risk management concepts, techniques and tools Alexander J. McNeil, Rüdiger Frey, Paul Embrechts |
| title_full_unstemmed | Quantitative risk management concepts, techniques and tools Alexander J. McNeil, Rüdiger Frey, Paul Embrechts |
| title_short | Quantitative risk management |
| title_sort | quantitative risk management concepts techniques and tools |
| title_sub | concepts, techniques and tools |
| topic | Risikomanagement (DE-588)4121590-4 gnd Finanzmathematik (DE-588)4017195-4 gnd Mathematische Methode (DE-588)4155620-3 gnd Finanzmanagement (DE-588)4139075-1 gnd Financial Engineering (DE-588)4208404-0 gnd Kreditrisiko (DE-588)4114309-7 gnd |
| topic_facet | Risikomanagement Finanzmathematik Mathematische Methode Finanzmanagement Financial Engineering Kreditrisiko |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028032131&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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