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Quantitative Risk Management: Concepts, Techniques, and Tools (Princeton Series in Finance) Hardcover – October 16, 2005

ISBN-13: 978-0691122557 ISBN-10: 0691122555
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Editorial Reviews

Review

One of the Top 10 Technical Books on Financial Engineering by Financial Engineering News for 2006

"Quantitative Risk Managment can be highly recommended to anyone looking for an excellent survey of the most important techniques and tools used in this rapidly growing field."--Holger Drees, Risk

"This book provides a state-of-the-art discussion of the three main categories of risk in financial markets, market risk, . . . credit risk . . . and operational risk. . . . This is a high level, but well-written treatment, rigorous (sometimes succinct), complete with theorems and proofs."--D.L. McLeish, Short Book Reviews of the International Statistical Institute

"Quantitative Risk Management is highly recommended for financial regulators. The statistical and mathematical tools facilitate a better understanding of the strengths and weaknesses of a useful range of advanced risk-management concepts and models, while the focus on aggregate risk enhances the publication's value to banking and insurance supervisors."--Hans Blommestein, The Financial Regulator

"A great summary of the latest techniques available within quantitative risk measurement. . . . [I]t is an excellent text to have on the shelf as a reference when your day job covers the whole spectrum of quantitative techniques in risk management."--Financial Engineering News

"Alexander McNeil, Rudiger Frey and Paul Embrechts have written a beautiful book. . . . [T]here is no book that can provide the type of rigorous, detailed, well balanced and relevant coverage of quantitative risk management topics that Quantitative Risk Management: Concepts, Techniques, and Tools offers. . . . I believe that this work may become the book on quantitative risk management. . . . [N]o book that I know of can provide better guidance."--Dr. Riccardo Rebonato, Global Association of Risk Professionals (GARP) Review

"This is a very impressive book on a rapidly growing field. It certainly helps to discover the forest in an area where a lot of trees are popping up daily."--Hans Bühlmann, SIAM Review

From the Back Cover

"This book is a compendium of the statistical arrows that should be in any quantitative risk manager's quiver. It includes extensive discussion of dynamic volatility models, extreme value theory, copulas, and credit risk. Academics, Ph.D. students, and quantitative practitioners will find many new and useful results in this important volume."--Robert F. Engle III, 2003 Nobel Laureate in Economic Sciences, Michael Armellino Professor in the Management of Financial Services at New York University's Stern School of Business

"This book provides a framework and a useful toolkit for analysis a wide variety of risk management problems. Common pitfalls are pointed out, and mathematical sophistication is used in pursuit of useful and usable solutions. Every financial institution has a risk management department that looks at aggregated portfolio-wide risks on longer time scales, and at risk exposure to large, or extreme, market movements. Risk managers are always on the lookout for good techniques to help them do their jobs. This very good book provides these techniques and addresses an important, and under-developed, area of practical research."--Martin Baxter, Nomura International

"McNeil, Frey, and Embrechts present a wide-ranging yet remarkably clear and coherent introduction to the modelling of financial risk. Unlike most finance texts, where the focus is on pricing individual instruments, the primary focus in this book is the statistical behavior of portfolios of risky instruments, which is, after all, the primary concern of risk management. This ought to be a core text in every risk manager's training, and a useful reference for experienced professionals."--Michael Gordy

"There is no book that provides the type of rigorous and detailed coverage of risk management topics that this book does. This could become the book on quantitative risk management."--Riccardo Rebonato, Royal Bank of Scotland, author of Modern Pricing of Interest-Rate Derivatives

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Product Details

  • Series: Princeton Series in Finance
  • Hardcover: 538 pages
  • Publisher: Princeton University Press (October 16, 2005)
  • Language: English
  • ISBN-10: 0691122555
  • ISBN-13: 978-0691122557
  • Product Dimensions: 9.5 x 6.5 x 1.3 inches
  • Shipping Weight: 2 pounds (View shipping rates and policies)
  • Average Customer Review: 3.7 out of 5 stars  See all reviews (10 customer reviews)
  • Amazon Best Sellers Rank: #881,287 in Books (See Top 100 in Books)

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Customer Reviews

3.7 out of 5 stars

Most Helpful Customer Reviews

22 of 22 people found the following review helpful By B. Peterson on July 24, 2007
Format: Hardcover Verified Purchase
Although not obvious, there is software available to implement the functionality described mathematically in the book. Alexander McNeil provides S-Plus code on his personal website, and there is an R port of that code on CRAN called QRMlib. Most of the provided software is on fitting fat-tailed distributions. This is all very useful in practice, if you care to be statistically precise. Unfortunately, many practitioners would clearly prefer rules of thumb to quantitative methods only usable with statistical software that doesn't run in Excel. Excellent theoretical text with solid backing software.
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78 of 95 people found the following review helpful By Jean Quasibob on January 19, 2006
Format: Hardcover
If you want to read a book on risk management, this may be not the book to read. This book is interesting as an applied math book for say some application in risk management but not as a risk management book. The main application of this book is credit risk. What does the reader learn ? Nothing about how to compute the spread of a CDO's tranche, nothing about how to manage correlation risk, nothing about how to manage spread risk, nothing about the real value of the calibrated intensity and nothing about the real value of the spreads. Needless to say, you will learn nothing about the new indices such as i-traxx for calibration. As a risk management book, it is a rather poor book. However, you will learn many things on time series, stochastic intensity models, copula and so on. In fact, the right title is "Mathematical and Statistical methods for risk management in view". Bearing in mind that this is an applied math book, it is well written and contains a lot of material that can be interesting. As a consequence, this book is rated with 1 star as a risk management book but with 4 stars as an applied math book.
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9 of 9 people found the following review helpful By Fangbing Wu on October 22, 2007
Format: Hardcover Verified Purchase
I'd add the word power in front of tools in the book title! Yes the book doesn't give you any step-by-step how to of doing any of the things like some have complained. Then again, it's not meant to be a how-to book. This is a "why" book and the authors explain the whys brilliantly. Even the chapters covering statistical background materials, the authors chose the exact level of details for coverage without wasting any pages. To appreciate the book, the reader does need a strong math background. Then every page of the book is worth it.
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7 of 8 people found the following review helpful By Dr. Grigory Sergeenko on June 8, 2007
Format: Hardcover
This is a typical theoretical book. With all pros and cons around that statement. As a mathematician I found it well written in terms of math introduction to the subject. BUT I would never recommend that book for the practical learning. It is SO FAR away from the practical quants everyday job, that one would never use that book. 3 stars= 5/2(theory)+1/2(practice)
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4 of 4 people found the following review helpful By S. Matthews on November 12, 2007
Format: Hardcover
I read this a while ago, and while I was extremely impressed with the theoretical development, and am very happy to have it in my library, I was also struck by the somewhat limited perspective. My background in part is in information theory and statistical learning, which means that I incline to a Bayesian view of uncertainty. But this is an absolutist 'frequentist' book; it does not even seem to be aware of a whole box of powerful theoretical tools that I know (it doesn't acknowledge them even to dismiss them).

I was fascinated recently to see that Ricardo Rebonato - in spite of quoted review above - seems to agree: in his new book (plight of the fortune tellers), he makes the same points that occured to me.
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Quantitative Risk Management: Concepts, Techniques, and Tools (Princeton Series in Finance)
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