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

by David Jamieson Bolder

Finance Risk Management Quantitative Finance Python Programming
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Credit-Risk Modelling brings together theory, diagnostics, practical examples, and Python-based numerical recipes in one reference. Written by David Jamieson Bolder, it offers readers an organized way to study the foundations and computational aspects of credit-risk modelling.

About This Book

Credit-Risk Modelling presents a structured treatment of the concepts and methods used to analyze credit risk.

The book covers theoretical foundations alongside diagnostic tools for examining and evaluating credit-risk models.

Practical examples connect the underlying ideas with applied modelling tasks.

Numerical recipes in Python provide a computational component for readers working with credit-risk methods and implementations.

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I will be using this book for: