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

by Alexander J. McNeil (Author), Rudiger Frey (Author), Paul Embrechts (Author)
3.0 out of 5 stars  See all reviews (1 customer review)
RRP: £59.00
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Quantitative Risk Management: Concepts, Techniques, and Tools (Princeton Series in Finance) + Simple Tools and Techniques for Enterprise Risk Management + Enterprise Risk Management: From Incentives to Controls (Wiley Finance)
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Product details

  • Hardcover: 538 pages
  • Publisher: Princeton University Press (26 Sep 2005)
  • Language English
  • ISBN-10: 0691122555
  • ISBN-13: 978-0691122557
  • Product Dimensions: 22.6 x 15.2 x 3.6 cm
  • Average Customer Review: 3.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon.co.uk Sales Rank: 131,387 in Books (See Bestsellers in Books)
  • See Complete Table of Contents

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Product Description

Review

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 Buhlmann SIAM Review )


Review

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 )

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Quantitative Risk Management: Concepts, Techniques, and Tools (Princeton Series in Finance)
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Average Customer Review
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Most Helpful Customer Reviews

 
2 of 2 people found the following review helpful:
3.0 out of 5 stars Good reference manual, incomplete for practical use, 16 Jun 2009
QRM is a technical book on risk management from a statistical point of view. It is definitely not a manual for practical implementation of QRM tools, so do not expect any how-tos. Rather, it is an excellent starting point for the risk manager who is keen on the technical aspects of risk measurement. Every chapter contains many references which point the reader to sources containing more detailed explanations. This book assumes a decent knowledge of statistics, particularly time series analysis. Also, the reader must be familiar with matrix algebra.

Good points: puts risk measurement into a formal, rigorous statistical framework; good overview of risk measurement topics; implementation in R of some tools is available for free as an R package.

Bad points: not very detailed in terms of how to implement many of the models; some chapters seem to be there more for completeness than for their practical value (I didn't find the chapter about copulas in particularly useful); too theoretical and very little emphasis on the practical side.

In summary, this book is for a risk manager who is very well trained in statistics and will be able (and willing) to implement the tools starting from the statistical concepts.
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