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O**E
ITEM AS DESCRIBED AND SHIPPED QUICKLY.
Highly Recommended. ITEM AS DESCRIBED AND SHIPPED QUICKLY. Thanks.
R**I
A solid book that should be read by Quants in Finance
In full disclosure, I know the author - Dr. Gunter Meissner is a friend and a collaborator. I have known Gunter for twenty years, and the quality of his work is top-notch. This book is not an exception. Correlation Risk Modelling and Management is highly relevant to financial engineers and quants who are designing models that might be utilized in hedge funds, as well as to risk managers, researchers, and those in academia who are in mathematical finance. This book would be a good addition to a financial mathematics graduate program.In many ways, this is a subject that has not been given enough attention at a deeper level. Gunter's book is a good remedy to this issue. Not only is the book well written and covers a lot of ground, but there are complete references, thus allowing the reader who wishes to do so, to go further into any subtopic.This book is already starting to create somewhat of a buzz. The Eurex held a seminar in Chicago in February 2014 featuring Gunter Meissner. In this seminar Dr. Meissner spoke on some of the topics in this book, and demonstrated some applications to specific contracts traded on the Eurex. The event was a tremendous success, and had a diverse crowd of hedge fund managers, private prop traders, institutional investors, consultants, and academic scholars.
T**Y
A Must-Have !
This is a really valuable book!!It gives a complete overview of all correlation models applied in Finance.The Pearson model is heavily criticized, rightfully so, and the limitations of the Gaussian copula, especially Vasicek’s OFGC are nicely pointed out. New correlation risk parameters, Cora and Gora, are introduced, which should be in the toolbox of every risk manager. The author believes that stochastic correlation modelling is the future, we will see.I recommend this book to anyone who wants to better understand the subject of correlation risk and its nuances.
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