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Credit-Risk Modelling

Theoretical Foundations, Diagnostic Tools, Practical Examples, and Numerical Recipes in Python

  • David JamiesonĀ Bolder

Table of contents

  1. Front Matter
    Pages i-xxxv
  2. David Jamieson Bolder
    Pages 1-38
  3. Part I

    1. Front Matter
      Pages 39-40
    2. David Jamieson Bolder
      Pages 41-83
    3. David Jamieson Bolder
      Pages 85-148
    4. David Jamieson Bolder
      Pages 149-227
    5. David Jamieson Bolder
      Pages 229-283
  4. Part II

    1. Front Matter
      Pages 285-286
    2. David Jamieson Bolder
      Pages 287-349
    3. David Jamieson Bolder
      Pages 351-427
    4. David Jamieson Bolder
      Pages 429-487
  5. Part III

    1. Front Matter
      Pages 489-489
    2. David Jamieson Bolder
      Pages 491-573
    3. David Jamieson Bolder
      Pages 575-635
  6. Back Matter
    Pages 637-684

About this book

Introduction

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.

Keywords

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

Authors and affiliations

  • David JamiesonĀ Bolder
    • 1
  1. 1.The World BankWashington, DCUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-94688-7
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Economics and Finance
  • Print ISBN 978-3-319-94687-0
  • Online ISBN 978-3-319-94688-7
  • Buy this book on publisher's site