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Chapter 1 [PDF]. Lando successfully guides the reader through the maze of a very active field of research by clearly identifying the leading problems and the attempts that have been made to solve these problems.
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At the same time, never does he neglect the statistical estimation of the models he presents. This is a very valuable book to any practitioner, student, or researcher in credit risk, written by one of the leading experts in the field.
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The Mathematical Sciences in 2025
Credit risk: modeling, valuation and hedging
The second chapter is about sampling and data preparation. What I like most about this chapter is that it shows how SAS Enterprise Miner could be used to facilitate the sampling and data pre-processing in such a simple and quick manner. These chapters do not only discuss some popular and key modeling techniques, but they also provide details about some essential performance measures for model evaluation. These chapters have a good balance between the underlying mathematical models and some topics about applying those techniques in practice.
For example, there are sections discussing PD model deployment, the estimation of downturn LGD, and the calculation of credit conversion factors in EAD models, etc. Stress testing is one of the topics highlighted by financial regulators.
Chapter 6 certainly helps readers to explore different types of stress testing approaches. If you are experienced in the area, this book provides insights for you to overcome some practical challenges.