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The Resource Credit-risk modelling : theoretical foundations, diagnostic tools, practical examples, and numerical recipes in Python, David Jamieson Bolder

Credit-risk modelling : theoretical foundations, diagnostic tools, practical examples, and numerical recipes in Python, David Jamieson Bolder

Label
Credit-risk modelling : theoretical foundations, diagnostic tools, practical examples, and numerical recipes in Python
Title
Credit-risk modelling
Title remainder
theoretical foundations, diagnostic tools, practical examples, and numerical recipes in Python
Statement of responsibility
David Jamieson Bolder
Creator
Author
Subject
Language
eng
Member of
Cataloging source
N$T
http://library.link/vocab/creatorName
Bolder, David
Dewey number
658.8/80151
Index
index present
LC call number
HG3751
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/subjectName
  • Credit
  • Financial risk management
  • Python (Computer program language)
Label
Credit-risk modelling : theoretical foundations, diagnostic tools, practical examples, and numerical recipes in Python, David Jamieson Bolder
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; Foreword; Preface; My Motivation; Transparency and Accessibility; Concreteness; Multiplicity of Perspective; Some Important Caveats; References; Acknowledgements; Contents; List of Figures; List of Tables; List of Algorithms; 1 Getting Started; 1.1 Alternative Perspectives; 1.1.1 Pricing or Risk-Management?; 1.1.2 Minding our P's and Q's; 1.1.3 Instruments or Portfolios?; 1.1.4 The Time Dimension; 1.1.5 Type of Credit-Risk Model; 1.1.6 Clarifying Our Perspective; 1.2 A Useful Dichotomy; 1.2.1 Modelling Implications; 1.2.2 Rare Events; 1.3 Seeing the Forest; 1.3.1 Modelling Frameworks
  • 1.3.2 Diagnostic Tools1.3.3 Estimation Techniques; 1.3.4 The Punchline; 1.4 Prerequisites; 1.5 Our Sample Portfolio; 1.6 A Quick Pre-Screening; 1.6.1 A Closer Look at Our Portfolio; 1.6.2 The Default-Loss Distribution; 1.6.3 Tail Probabilities and Risk Measures; 1.6.4 Decomposing Risk; 1.6.5 Summing Up; 1.7 Final Thoughts; References; Part I Modelling Frameworks; Reference; 2 A Natural First Step; 2.1 Motivating a Default Model; A Bit of Structure; 2.1.1 Two Instruments; 2.1.2 Multiple Instruments; 2.1.3 Dependence; 2.2 Adding Formality; 2.2.1 An Important Aside; 2.2.2 A Numerical Solution
  • 2.2.2.1 Bernoulli Trials2.2.2.2 Practical Details; 2.2.2.3 Some Results; 2.2.3 An Analytical Approach; 2.2.3.1 Putting It into Action; 2.2.3.2 Comparing Key Assumptions; 2.3 Convergence Properties; Convergence in Probability; Almost-Sure Convergence; Cutting to the Chase; 2.4 Another Entry Point; A Numerical Implementation; The Analytic Model; The Law of Rare Events; 2.5 Final Thoughts; References; 3 Mixture or Actuarial Models; 3.1 Binomial-Mixture Models; Conditional Independence; Default-Correlation Coefficient; The Distribution of DN; Convergence Properties
  • 3.1.1 The Beta-Binomial Mixture Model3.1.1.1 Beta-Parameter Calibration; 3.1.1.2 Back to Our Example; 3.1.1.3 Non-homogeneous Exposures; 3.1.2 The Logit- and Probit-Normal Mixture Models; 3.1.2.1 Deriving the Mixture Distributions; 3.1.2.2 Numerical Integration; 3.1.2.3 Logit- and Probit-Normal Calibration; 3.1.2.4 Logit- and Probit-Normal Results; 3.2 Poisson-Mixture Models; 3.2.1 The Poisson-Gamma Approach; 3.2.1.1 Calibrating the Poisson-Gamma Mixture Model; 3.2.1.2 A Quick and Dirty Calibration; 3.2.1.3 Poisson-Gamma Results; 3.2.2 Other Poisson-Mixture Approaches
  • 3.2.2.1 A Calibration Comparison3.2.3 Poisson-Mixture Comparison; 3.3 CreditRisk+; 3.3.1 A One-Factor Implementation; 3.3.2 A Multi-Factor CreditRisk+ Example; 3.4 Final Thoughts; References; 4 Threshold Models; 4.1 The Gaussian Model; 4.1.1 The Latent Variable; 4.1.2 Introducing Dependence; 4.1.3 The Default Trigger; 4.1.4 Conditionality; 4.1.5 Default Correlation; 4.1.6 Calibration; 4.1.7 Gaussian Model Results; 4.2 The Limit-Loss Distribution; 4.2.1 The Limit-Loss Density; 4.2.2 Analytic Gaussian Results; 4.3 Tail Dependence; 4.3.1 The Tail-Dependence Coefficient
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783319946870
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1061148117
  • (OCoLC)1061148117
Label
Credit-risk modelling : theoretical foundations, diagnostic tools, practical examples, and numerical recipes in Python, David Jamieson Bolder
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; Foreword; Preface; My Motivation; Transparency and Accessibility; Concreteness; Multiplicity of Perspective; Some Important Caveats; References; Acknowledgements; Contents; List of Figures; List of Tables; List of Algorithms; 1 Getting Started; 1.1 Alternative Perspectives; 1.1.1 Pricing or Risk-Management?; 1.1.2 Minding our P's and Q's; 1.1.3 Instruments or Portfolios?; 1.1.4 The Time Dimension; 1.1.5 Type of Credit-Risk Model; 1.1.6 Clarifying Our Perspective; 1.2 A Useful Dichotomy; 1.2.1 Modelling Implications; 1.2.2 Rare Events; 1.3 Seeing the Forest; 1.3.1 Modelling Frameworks
  • 1.3.2 Diagnostic Tools1.3.3 Estimation Techniques; 1.3.4 The Punchline; 1.4 Prerequisites; 1.5 Our Sample Portfolio; 1.6 A Quick Pre-Screening; 1.6.1 A Closer Look at Our Portfolio; 1.6.2 The Default-Loss Distribution; 1.6.3 Tail Probabilities and Risk Measures; 1.6.4 Decomposing Risk; 1.6.5 Summing Up; 1.7 Final Thoughts; References; Part I Modelling Frameworks; Reference; 2 A Natural First Step; 2.1 Motivating a Default Model; A Bit of Structure; 2.1.1 Two Instruments; 2.1.2 Multiple Instruments; 2.1.3 Dependence; 2.2 Adding Formality; 2.2.1 An Important Aside; 2.2.2 A Numerical Solution
  • 2.2.2.1 Bernoulli Trials2.2.2.2 Practical Details; 2.2.2.3 Some Results; 2.2.3 An Analytical Approach; 2.2.3.1 Putting It into Action; 2.2.3.2 Comparing Key Assumptions; 2.3 Convergence Properties; Convergence in Probability; Almost-Sure Convergence; Cutting to the Chase; 2.4 Another Entry Point; A Numerical Implementation; The Analytic Model; The Law of Rare Events; 2.5 Final Thoughts; References; 3 Mixture or Actuarial Models; 3.1 Binomial-Mixture Models; Conditional Independence; Default-Correlation Coefficient; The Distribution of DN; Convergence Properties
  • 3.1.1 The Beta-Binomial Mixture Model3.1.1.1 Beta-Parameter Calibration; 3.1.1.2 Back to Our Example; 3.1.1.3 Non-homogeneous Exposures; 3.1.2 The Logit- and Probit-Normal Mixture Models; 3.1.2.1 Deriving the Mixture Distributions; 3.1.2.2 Numerical Integration; 3.1.2.3 Logit- and Probit-Normal Calibration; 3.1.2.4 Logit- and Probit-Normal Results; 3.2 Poisson-Mixture Models; 3.2.1 The Poisson-Gamma Approach; 3.2.1.1 Calibrating the Poisson-Gamma Mixture Model; 3.2.1.2 A Quick and Dirty Calibration; 3.2.1.3 Poisson-Gamma Results; 3.2.2 Other Poisson-Mixture Approaches
  • 3.2.2.1 A Calibration Comparison3.2.3 Poisson-Mixture Comparison; 3.3 CreditRisk+; 3.3.1 A One-Factor Implementation; 3.3.2 A Multi-Factor CreditRisk+ Example; 3.4 Final Thoughts; References; 4 Threshold Models; 4.1 The Gaussian Model; 4.1.1 The Latent Variable; 4.1.2 Introducing Dependence; 4.1.3 The Default Trigger; 4.1.4 Conditionality; 4.1.5 Default Correlation; 4.1.6 Calibration; 4.1.7 Gaussian Model Results; 4.2 The Limit-Loss Distribution; 4.2.1 The Limit-Loss Density; 4.2.2 Analytic Gaussian Results; 4.3 Tail Dependence; 4.3.1 The Tail-Dependence Coefficient
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783319946870
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1061148117
  • (OCoLC)1061148117

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