Demonstrates a broad range of state-of-the-art credit-risk models and underscores their interlinkages
Includes extensive Python code to bring the models, diagnostic tools, and estimation of key inputs parameters to life
Combination of mathematical foundations and practical Python code implementation enriches the reader´s understanding and competence in this important field
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.
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 deillegalscription 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.|
Demonstrates a broad range of state-of-the-art credit-risk models and underscores their interlinkages
Includes extensive Python code to bring the models, diagnostic tools, and estimation of key inputs parameters to life
Combination of mathematical foundations and practical Python code implementation enriches the reader´s understanding and competence in this important field
Getting Started.- Part I Modelling Frameworks.- A Natural First Step.-Mixture or Actuarial Models.- Threshold Models.-The Genesis of Credit-Risk Modelling.- Part II Diagnostic Tools.- A Regulatory Perspective.- Risk Attribution.- Monte Carlo Methods.- Part III Parameter Estimation.- Default Probabilities.- Default and Asset Correlation.
"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)
Getting Started.- Part I Modelling Frameworks.- A Natural First Step.-Mixture or Actuarial Models.- Threshold Models.-The Genesis of Credit-Risk Modelling.- Part II Diagnostic Tools.- A Regulatory Perspective.- Risk Attribution.- Monte Carlo Methods.- Part III Parameter Estimation.- Default Probabilities.- Default and Asset Correlation.
"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)
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.
Über den Autor
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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Inhaltsverzeichnis
Getting Started.- Part I Modelling Frameworks.- A Natural First Step.-Mixture or Actuarial Models.- Threshold Models.-The Genesis of Credit-Risk Modelling.- Part II Diagnostic Tools.- A Regulatory Perspective.- Risk Attribution.- Monte Carlo Methods.- Part III Parameter Estimation.- Default Probabilities.- Default and Asset Correlation.
Klappentext
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 deillegalscription 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.
Demonstrates a broad range of state-of-the-art credit-risk models and underscores their interlinkages
Includes extensive Python code to bring the models, diagnostic tools, and estimation of key inputs parameters to life
Combination of mathematical foundations and practical Python code implementation enriches the reader's understanding and competence in this important field
