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

Buena combinación teórico práctica

Good book.

This is one of the best books available on this topic. I enjoyed reading the book! The mathematical background has been nicely explained. Anyone with minimum working knowledge in Maths and Stats can easily understand the concepts.

Good book so far. Theoretical and practical as well.

I guess it's ok if one makes a typo in the code and it's understandable. But the code seemed have their logic confused as well. Sometimes a function introduces an argument that is not used in the function body. Sometimes dimensions of a matrix was forgotten and flattened to an array. Sometimes a constant is treated like an array and called by array related methods. Maybe I have just misunderstood things, but the code I tried to replicate in my Jupyter notebook rarely runs. If the author can put some comments and expected intermediate sample results that would be quite helpful. Otherwise I found these code just confuses the content of the book. Other than the code, the book in general can do with some thoughtful editing. For instance, the topics for subchapters are usually titled "some thoughts", "some details". These are are not informative to the readers when scanning over the table of contents. Paragraphs can be shortened with repeated sentences sending the same message. Sometimes obscure and vague figurative vocabulary is used and I found myself keeping a dictionary handy. Sometimes the sentence can get quite fluffy. (i.e. "Our thematic categorization is intended to inform three distinct questions. The first question, associated with the initial theme...", I didn't know what that mean and by the way, I wasn't able to find the second and the third question, neither understood what the themes were.) And sentences in general can do with less comma, adornment, and pomposity (i.e. "Default is, quite simply, an inability, or unwillingness, of an obligor to meet its obligations") However it's not all negatives. The math part so far are quite straightforward and well derived. (Although I somehow think the author has the basic variance formula var(x) = E[x^2] - E[x]^2 either consistently typo-ed or mistook) The arrangement of the chapters are clear. The messages are conveyed consistently and the connections between the chapters are strong. There is no doubt it's a blessing for the readers to have an experienced practitioner like the author. But if the book can be edited for a clearer messages this will be an even more pleasant text to study.