Credit Risk Modelling: Theoretical Foundations, Diagnostic Tools, Practical Examples, and Numerical Recipes in Python - Rilegato

Bolder, David Jamieson

 
9783319946870: Credit Risk Modelling: Theoretical Foundations, Diagnostic Tools, Practical Examples, and Numerical Recipes in Python
Rilegato
ISBN 10: 3319946870 ISBN 13: 9783319946870
Casa editrice: Springer Nature, 2018

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sull?autore

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.



Dalla quarta di copertina

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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer Nature
Data di pubblicazione
2018
Lingua
Inglese
ISBN 10 
3319946870
ISBN 13 
9783319946870
Rilegatura
Copertina rigida
Numero edizione
1
Numero di pagine
684
Contatto del produttore
ProductSafety@springernature.com
ProductSafety@springernature.com

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Altre edizioni note dello stesso titolo

9783030069001: Credit-Risk Modelling: Theoretical Foundations, Diagnostic Tools, Practical Examples, and Numerical Recipes in Python

Edizione in evidenza

ISBN 10:  3030069001 ISBN 13:  9783030069001
Casa editrice: Springer Nature, 2019
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