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Credit-Risk Modelling
Theoretical Foundations, Diagnostic Tools, Practical Examples, and Numerical Recipes in Python
Buch von David Jamieson Bolder
Sprache: Englisch

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Beschreibung
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.
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.
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.

Zusammenfassung

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

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.

Details
Erscheinungsjahr: 2018
Fachbereich: Management
Genre: Recht, Sozialwissenschaften, Wirtschaft
Rubrik: Recht & Wirtschaft
Medium: Buch
Inhalt: xxxv
684 S.
130 farbige Illustr.
684 p. 130 illus. in color.
ISBN-13: 9783319946870
ISBN-10: 3319946870
Sprache: Englisch
Herstellernummer: 978-3-319-94687-0
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Bolder, David Jamieson
Auflage: 1st ed. 2018
Hersteller: Springer International Publishing
Springer International Publishing AG
Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße: 241 x 160 x 44 mm
Von/Mit: David Jamieson Bolder
Erscheinungsdatum: 12.11.2018
Gewicht: 1,232 kg
Artikel-ID: 113780820
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.

Zusammenfassung

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

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.

Details
Erscheinungsjahr: 2018
Fachbereich: Management
Genre: Recht, Sozialwissenschaften, Wirtschaft
Rubrik: Recht & Wirtschaft
Medium: Buch
Inhalt: xxxv
684 S.
130 farbige Illustr.
684 p. 130 illus. in color.
ISBN-13: 9783319946870
ISBN-10: 3319946870
Sprache: Englisch
Herstellernummer: 978-3-319-94687-0
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Bolder, David Jamieson
Auflage: 1st ed. 2018
Hersteller: Springer International Publishing
Springer International Publishing AG
Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße: 241 x 160 x 44 mm
Von/Mit: David Jamieson Bolder
Erscheinungsdatum: 12.11.2018
Gewicht: 1,232 kg
Artikel-ID: 113780820
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