Beschreibung Credit-Risk Modelling: Theoretical Foundations, Diagnostic Tools, Practical Examples, and Numerical Recipes in Python. 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.
Credit-Risk Modelling - Theoretical Foundations ~ Credit-Risk Modelling Theoretical Foundations, Diagnostic Tools, Practical Examples, and Numerical Recipes in Python. Authors: Bolder, David Jamieson Free Preview. 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 .
Credit-Risk Modelling / SpringerLink ~ Credit-Risk Modelling Theoretical Foundations, Diagnostic Tools, Practical Examples, and Numerical Recipes in Python. Authors (view affiliations) David Jamieson Bolder; Book . 2 Mentions; 17k Downloads; Download book PDF. Download book EPUB. Chapters Table of contents (10 chapters) About About this book; Table of contents . Search within book. Front Matter. Pages i-xxxv. PDF. Getting Started .
David Jamieson Bolder - Credit-Risk Modelling_ Theoretical ~ David Jamieson Bolder - Credit-Risk Modelling_ Theoretical Foundations, Diagnostic Tools, Practical Examples, and Numerical Recipes in Python (2018, Springer International Publishing).pdf - Free ebook download as PDF File (.pdf), Text File (.txt) or read book online for free.
Credit-Risk Modelling: Theoretical Foundations, Diagnostic ~ Credit-Risk Modelling: Theoretical Foundations, Diagnostic Tools, Practical Examples, and Numerical Recipes in Python David Jamieson Bolder The risk of counterparty default in banking, insurance, institutional, and pension-fund portfolios is an area of ongoing and increasing importance for finance practitioners.
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: Credit-Risk Modelling: Theoretical Foundations ~ Credit-Risk Modelling: Theoretical Foundations, Diagnostic Tools, Practical Examples, and Numerical Recipes in Python - Kindle edition by Bolder, David Jamieson. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Credit-Risk Modelling: Theoretical Foundations, Diagnostic Tools, Practical Examples .
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