Credit-Risk Modelling

1. Front Matter

   Pages i-xxxv

   PDF

2. Getting Started

   • David Jamieson Bolder

   Pages 1-38

3. Part I

   1. Front Matter

      Pages 39-40

      PDF

   2. A Natural First Step

      • David Jamieson Bolder

      Pages 41-83

   3. Mixture or Actuarial Models

      • David Jamieson Bolder

      Pages 85-148

   4. Threshold Models

      • David Jamieson Bolder

      Pages 149-227

   5. The Genesis of Credit-Risk Modelling

      • David Jamieson Bolder

      Pages 229-283

4. Part II

   1. Front Matter

      Pages 285-286

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   2. A Regulatory Perspective

      • David Jamieson Bolder

      Pages 287-349

   3. Risk Attribution

      • David Jamieson Bolder

      Pages 351-427

   4. Monte Carlo Methods

      • David Jamieson Bolder

      Pages 429-487

5. Part III

   1. Front Matter

      Pages 489-489

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   2. Default Probabilities

      • David Jamieson Bolder

      Pages 491-573

   3. Default and Asset Correlation

      • David Jamieson Bolder

      Pages 575-635

6. Back Matter

   Pages 637-684

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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.

• python code
• monte carlo
• financial engineering
• model risk
• risk modeling
• default risk
• binomial models
• poisson models
• asset correlation
• black scholes
• markov chains
• t distribution
• quantitative finance
• banking

“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)

• The World Bank, Washington, DC, USA

  David Jamieson Bolder

David Jamieson Bolder is currently head of the World Bank Group’s (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.

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