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Credit-Risk Modelling: Theoretical Foundations, Diagnostic Tools, Practical Examples, and Numerical Recipes in Python 2018 ed. [Kietas viršelis]

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  • Formatas: Hardback, 684 pages, aukštis x plotis: 235x155 mm, weight: 1238 g, 130 Illustrations, color; XXXV, 684 p. 130 illus. in color., 1 Hardback
  • Išleidimo metai: 12-Nov-2018
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3319946870
  • ISBN-13: 9783319946870
Kitos knygos pagal šią temą:
Credit-Risk Modelling: Theoretical Foundations, Diagnostic Tools, Practical Examples, and Numerical Recipes in Python 2018 ed.Didesnis paveikslėlis
  • Formatas: Hardback, 684 pages, aukštis x plotis: 235x155 mm, weight: 1238 g, 130 Illustrations, color; XXXV, 684 p. 130 illus. in color., 1 Hardback
  • Išleidimo metai: 12-Nov-2018
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3319946870
  • ISBN-13: 9783319946870
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783319946870.html
The risk of counterparty default in banking, insurance, institutional, and pension-fund portfolios is an area of ongoing and increasing importance for finance practitioners. It is, unfortunately, a topic with a high degree of technical complexity. Addressing this challenge, this book provides a comprehensive and attainable mathematical and statistical discussion of a broad range of existing default-risk models. Model description and derivation, however, is only part of the story. Through use of exhaustive practical examples and extensive code illustrations in the Python programming language, this work also explicitly shows the reader how these models are implemented. Bringing these complex approaches to life by combining the technical details with actual real-life Python code reduces the burden of model complexity and enhances accessibility to this decidedly specialized field of study. The entire work is also liberally supplemented with model-diagnostic, calibration, and parameter-estimation techniques to assist the quantitative analyst in day-to-day implementation as well as in mitigating model risk. Written by an active and experienced practitioner, it is an invaluable learning resource and reference text for financial-risk practitioners and an excellent source for advanced undergraduate and graduate students seeking to acquire knowledge of the key elements of this discipline.

Recenzijos

The book is easy to read, the models and techniques are illustrated in detail and with complete derivations, making the volume accessible for self-study. (Claudio Fontana, zbMATH 1422.91012, 2019)

1 Getting Started
1(39)
1.1 Alternative Perspectives
3(8)
1.1.1 Pricing or Risk-Management?
3(3)
1.1.2 Minding our P's and Q's
6(1)
1.1.3 Instruments or Portfolios?
7(2)
1.1.4 The Time Dimension
9(1)
1.1.5 Type of Credit-Risk Model
10(1)
1.1.6 Clarifying Our Perspective
11(1)
1.2 A Useful Dichotomy
11(7)
1.2.1 Modelling Implications
13(2)
1.2.2 Rare Events
15(3)
1.3 Seeing the Forest
18(6)
1.3.1 Modelling Frameworks
19(2)
1.3.2 Diagnostic Tools
21(2)
1.3.3 Estimation Techniques
23(1)
1.3.4 The Punchline
24(1)
1.4 Prerequisites
24(2)
1.5 Our Sample Portfolio
26(1)
1.6 A Quick Pre-Screening
27(10)
1.6.1 A Closer Look at Our Portfolio
27(2)
1.6.2 The Default-Loss Distribution
29(1)
1.6.3 Tail Probabilities and Risk Measures
30(3)
1.6.4 Decomposing Risk
33(4)
1.6.5 Summing Up
37(1)
1.7 Final Thoughts
37(1)
References
38(2)
Part I Modelling Frameworks
Reference
40(1)
2 A Natural First Step
41(44)
2.1 Motivating a Default Model
42(8)
2.1.1 Two Instruments
45(1)
2.1.2 Multiple Instruments
46(4)
2.1.3 Dependence
50(1)
2.2 Adding Formality
50(17)
2.2.1 An Important Aside
53(2)
2.2.2 A Numerical Solution
55(7)
2.2.3 An Analytical Approach
62(5)
2.3 Convergence Properties
67(6)
2.4 Another Entry Point
73(9)
2.5 Final Thoughts
82(1)
References
82(3)
3 Mixture or Actuarial Models
85(64)
3.1 Binomial-Mixture Models
86(27)
3.1.1 The Beta-Binomial Mixture Model
92(9)
3.1.2 The Logit- and Probit-Normal Mixture Models
101(12)
3.2 Poisson-Mixture Models
113(18)
3.2.1 The Poisson-Gamma Approach
115(10)
3.2.2 Other Poisson-Mixture Approaches
125(4)
3.2.3 Poisson-Mixture Comparison
129(2)
3.3 CreditRisk+
131(16)
3.3.1 A One-Factor Implementation
131(10)
3.3.2 A Multi-Factor CreditRisk+ Example
141(6)
3.4 Final Thoughts
147(1)
References
148(1)
4 Threshold Models
149(80)
4.1 The Gaussian Model
150(15)
4.1.1 The Latent Variable
150(2)
4.1.2 Introducing Dependence
152(2)
4.1.3 The Default Trigger
154(1)
4.1.4 Conditionality
155(3)
4.1.5 Default Correlation
158(3)
4.1.6 Calibration
161(1)
4.1.7 Gaussian Model Results
162(3)
4.2 The Limit-Loss Distribution
165(10)
4.2.1 The Limit-Loss Density
170(2)
4.2.2 Analytic Gaussian Results
172(3)
4.3 Tail Dependence
175(7)
4.3.1 The Tail-Dependence Coefficient
176(3)
4.3.2 Gaussian Copula Tail-Dependence
179(1)
4.3.3 t-Copula Tail-Dependence
180(2)
4.4 The t-Distributed Approach
182(11)
4.4.1 A Revised Latent-Variable Definition
182(4)
4.4.2 Back to Default Correlation
186(2)
4.4.3 The Calibration Question
188(2)
4.4.4 Implementing the t-Threshold Model
190(3)
4.4.5 Pausing for a Breather
193(1)
4.5 Normal-Variance Mixture Models
193(18)
4.5.1 Computing Default Correlation
197(1)
4.5.2 Higher Moments
198(2)
4.5.3 Two Concrete Cases
200(1)
4.5.4 The Variance-Gamma Model
201(1)
4.5.5 The Generalized Hyperbolic Case
202(2)
4.5.6 A Fly in the Ointment
204(2)
4.5.7 Concrete Normal-Variance Results
206(5)
4.6 The Canonical Multi-Factor Setting
211(7)
4.6.1 The Gaussian Approach
211(3)
4.6.2 The Normal-Variance-Mixture Set-Up
214(4)
4.7 A Practical Multi-Factor Example
218(7)
4.7.1 Understanding the Nested State-Variable Definition
219(2)
4.7.2 Selecting Model Parameters
221(3)
4.7.3 Multivariate Risk Measures
224(1)
4.8 Final Thoughts
225(1)
References
226(3)
5 The Genesis of Credit-Risk Modelling
229(57)
5.1 Merton's Idea
230(10)
5.1.1 Introducing Asset Dynamics
233(3)
5.1.2 Distance to Default
236(2)
5.1.3 Incorporating Equity Information
238(2)
5.2 Exploring Geometric Brownian Motion
240(5)
5.3 Multiple Obligors
245(2)
5.3.1 Two Choices
246(1)
5.4 The Indirect Approach
247(8)
5.4.1 A Surprising Simplification
249(2)
5.4.2 Inferring Key Inputs
251(1)
5.4.3 Simulating the Indirect Approach
252(3)
5.5 The Direct Approach
255(25)
5.5.1 Expected Value of An,T
257(2)
5.5.2 Variance and Volatility of An,T
259(2)
5.5.3 Covariance and Correlation of An,T and Am,T
261(2)
5.5.4 Default Correlation Between Firms n and m
263(2)
5.5.5 Collecting the Results
265(1)
5.5.6 The Task of Calibration
265(5)
5.5.7 A Direct-Approach Inventory
270(1)
5.5.8 A Small Practical Example
270(10)
5.6 Final Thoughts
280(2)
References
282(4)
Part II Diagnostic Tools
References
286(1)
6 A Regulatory Perspective
287(64)
6.1 The Basel Accords
288(15)
6.1.1 Basel IRB
290(2)
6.1.2 The Basic Structure
292(3)
6.1.3 A Number of Important Details
295(5)
6.1.4 The Full Story
300(3)
6.2 IRB in Action
303(6)
6.2.1 Some Foreshadowing
306(3)
6.3 The Granularity Adjustment
309(39)
6.3.1 A First Try
311(1)
6.3.2 A Complicated Add-On
312(5)
6.3.3 The Granularity Adjustment
317(1)
6.3.4 The One-Factor Gaussian Case
318(7)
6.3.5 Getting a Bit More Concrete
325(3)
6.3.6 The CreditRisk+ Case
328(12)
6.3.7 A Final Experiment
340(6)
6.3.8 Final Thoughts
346(2)
References
348(3)
7 Risk Attribution
351(78)
7.1 The Main Idea
352(3)
7.2 A Surprising Relationship
355(13)
7.2.1 The Justification
357(4)
7.2.2 A Direct Algorithm
361(3)
7.2.3 Some Illustrative Results
364(2)
7.2.4 A Shrewd Suggestion
366(2)
7.3 The Normal Approximation
368(4)
7.4 Introducing the Saddlepoint Approximation
372(12)
7.4.1 The Intuition
373(3)
7.4.2 The Density Approximation
376(2)
7.4.3 The Tail Probability Approximation
378(3)
7.4.4 Expected Shortfall
381(1)
7.4.5 A Bit of Organization
382(2)
7.5 Concrete Saddlepoint Details
384(10)
7.5.1 The Saddlepoint Density
388(3)
7.5.2 Tail Probabilities and Shortfall Integralls
391(1)
7.5.3 A Quick Aside
392(1)
7.5.4 Illustrative Results
393(1)
7.6 Obligor-Level Risk Contributions
394(12)
7.6.1 The VaR Contributions
395(5)
7.6.2 Shortfall Contributions
400(6)
7.7 The Conditionally Independent Saddlepoint Approximation
406(15)
7.7.1 Implementation
412(3)
7.7.2 A Multi-Model Example
415(5)
7.7.3 Computational Burden
420(1)
7.8 An Interesting Connection
421(5)
7.9 Final Thoughts
426(1)
References
426(3)
8 Monte Carlo Methods
429(62)
8.1 Brains or Brawn?
430(1)
8.2 A Silly, But Informative Problem
431(8)
8.3 The Monte Carlo Method
439(6)
8.3.1 Monte Carlo in Finance
440(4)
8.3.2 Dealing with Slowness
444(1)
8.4 Interval Estimation
445(5)
8.4.1 A Rough, But Workable Solution
445(2)
8.4.2 An Example of Convergence Analysis
447(3)
8.4.3 Taking Stock
450(1)
8.5 Variance-Reduction Techniques
450(35)
8.5.1 Introducing Importance Sampling
451(2)
8.5.2 Setting Up the Problem
453(4)
8.5.3 The Esscher Transform
457(3)
8.5.4 Finding θ
460(2)
8.5.5 Implementing the Twist
462(5)
8.5.6 Shifting the Mean
467(7)
8.5.7 Yet Another Twist
474(2)
8.5.8 Tying Up Loose Ends
476(3)
8.5.9 Does It Work?
479(6)
8.6 Final Thoughts
485(1)
References
486(5)
Part III Parameter Estimation
9 Default Probabilities
491(84)
9.1 Some Preliminary Motivation
492(6)
9.1.1 A More Nuanced Perspective
493(5)
9.2 Estimation
498(46)
9.2.1 A Useful Mathematical Object
499(7)
9.2.2 Applying This Idea
506(2)
9.2.3 Cohort Approach
508(3)
9.2.4 Hazard-Rate Approach
511(1)
9.2.5 Getting More Practical
512(1)
9.2.6 Generating Markov-Chain Outcomes
513(5)
9.2.7 Point Estimates and Transition Statistics
518(5)
9.2.8 Describing Uncertainty
523(15)
9.2.9 Interval Estimates
538(3)
9.2.10 Risk-Metric Implications
541(3)
9.3 Risk-Neutral Default Probabilities
544(23)
9.3.1 Basic Cash-Flow Analysis
544(3)
9.3.2 Introducing Default Risk
547(6)
9.3.3 Incorporating Default Risk
553(4)
9.3.4 Inferring Hazard Rates
557(4)
9.3.5 A Concrete Example
561(6)
9.4 Back to Our P's and Q's
567(4)
9.5 Final Thoughts
571(1)
References
571(4)
10 Default and Asset Correlation
575(62)
10.1 Revisiting Default Correlation
576(4)
10.2 Simulating a Dataset
580(9)
10.2.1 A Familiar Setting
581(6)
10.2.2 The Actual Results
587(2)
10.3 The Method of Moments
589(6)
10.3.1 The Threshold Case
593(2)
10.4 Likelihood Approach
595(20)
10.4.1 The Basic Insight
596(2)
10.4.2 A One-Parameter Example
598(4)
10.4.3 Another Example
602(4)
10.4.4 A More Complicated Situation
606(9)
10.5 Transition Likelihood Approach
615(18)
10.5.1 The Elliptical Copula
616(6)
10.5.2 The Log-Likelihood Kernel
622(4)
10.5.3 Inferring the State Variables
626(2)
10.5.4 A Final Example
628(5)
10.6 Final Thoughts
633(1)
References
634(3)
A The t-Distribution
637(12)
A.1 The Chi-Squared Distribution
638(1)
A.2 Toward the t-Distribution
639(4)
A.3 Simulating Correlated t Variates
643(4)
A.4 A Quick Example
647(2)
B The Black-Scholes Formula
649(10)
B.1 Changing Probability Measures
649(4)
B.2 Solving the Stochastic Differential Equation
653(2)
B.3 Evaluating the Integral
655(3)
B.4 The Final Result
658(1)
C Markov Chains
659(8)
C.1 Some Background
659(2)
C.2 Some Useful Results
661(2)
C.3 Ergodicity
663(4)
D The Python Code Library
667(8)
D.1 Explaining Some Choices
668(1)
D.2 The Library Structure
669(2)
D.3 An Example
671(1)
D.4 Sample Exercises
672(1)
References
673(2)
Index 675(6)
Author Index 681
David Jamieson Bolder is currently head of the World Bank Groups (WBG) model-risk function. Prior to this appointment, he provided analytic support to the Bank for International Settlements (BIS) treasury and asset-management functions and worked in quantitative roles at the Bank of Canada, the World Bank Treasury, and the European Bank for Reconstruction and Development. He has authored numerous papers, articles, and chapters in books on financial modelling, stochastic simulation, and optimization. He has also published a comprehensive book on fixed-income portfolio analytics. His career has focused on the application of mathematical techniques towards informing decision-making in the areas of sovereign-debt, pension-fund, portfolio-risk, and foreign-reserve management.