An Introduction to Copulas (Springer Series in Statistics)

This is very high quality and well written. In that sense, I should probably give it 4 or 5 stars. So I apologize for that. My complaint is that it doesn't give enough practical understanding and examples. It just dives right in to the thick and heavy notation and proofs! In other words, a little merciful watering down (especially for the first few key concepts) couldn't have hurt.I do not recommend it for a professional who is learning how to model data correlation at work. This book is too harsh for self study.I do recommend it for an academic setting. An enthusiastic professor could help teach the concepts, and then assign problems for homework. This would even be appropriate at the undergrad level for a higher level course.

Text that explains the theory behind Copulas. This statistical tool made millions in stock market

As expected, the reference on copulas is very technical. Demands some skills on statistics (statistical mathematics, calculus, probability and inference).

This may be good as a mathematical treatise, but as a practitioner, I did not find it at all helpful.

Brace yourselves for thorough mathematical proofs. Nonetheless, great introduction into the topic.

This is the second edition of THE book on copulas; there are others, eg Joe, and Drouet and Kotz, but this is the best reference work I know of on this topic. The second edition does not frankly have a huge amount of new content; the most obvious is the very useful set of scatterplots used to illustrate the appearance of copulas. This I think shows up some properties like symmetry better than equations could do. If I had purchased the first edition, I probably wouldn't feel it had been worth upgrading---it depends on how deeply you are `into' this topic. On the other hand it's reassuring that there hasn't been a lot of recent work we didn't know about!I thoroughly recommend this book, and, if I meet the author, I shall reckon that he owes me a drink.

Couldn't agree more with A Customer. The style is crisp so that at times you find yourself pausing for half an hour over some line in a proof only to find that it was obvious all along. But that is good. Gets you thinking about copulas. By the end of the book you have a very satisfying intuitive feel for the subject with concepts like the distribution of mass throughout the copula making such heuristic sense. As an actuary working in insurance, this is perfect for my needs.

Ein absolutes Referenzwerk.Anschaulicher Einstieg, mathematisch sauber.Schafft die Grundlagen für tiefgehendere Literatur (wie Multivariate Models and Multivariate Dependence Concepts, H. Joe).Allen Anwendern sei jedoch eher "Quantitative Risk Management: Concepts, Techniques, and Tools" empfohlen, da dort die gängigsten Anwendungen der Copulas aufgezeigt werden.

コピュラは統計学の中でも新しい概念であるためか、適当な入門書がない。その中で本書はほぼ唯一の入門書といってよい。中身は、図が多くて分かりやすい。最初のほうは統計学と無関係そうな内容が続くが、後半は実務的なコピュラも豊富に出てくる。