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Credit-Risk Modelling - Theoretical Foundations, Diagnostic Tools, Practical Examples, and Numerical Recipes in Python (Hardcover, 1st ed. 2018)
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Credit-Risk Modelling - Theoretical Foundations, Diagnostic Tools, Practical Examples, and Numerical Recipes in Python (Hardcover, 1st ed. 2018)
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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.
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