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

by Alexander J. McNeil, Rudiger Frey, Paul Embrechts

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Product Description

The implementation of sound quantitative risk models is a vital concern for all financial institutions, and this trend has accelerated in recent years with regulatory processes such as Basel II. This book provides a comprehensive treatment of the theoretical concepts and modelling techniques of quantitative risk management and equips readers--whether financial risk analysts, actuaries, regulators, or students of quantitative finance--with practical tools to solve real-world problems. The authors cover methods for market, credit, and operational risk modelling; place standard industry approaches on a more formal footing; and describe recent developments that go beyond, and address main deficiencies of, current practice.

The book's methodology draws on diverse quantitative disciplines, from mathematical finance through statistics and econometrics to actuarial mathematics. Main concepts discussed include loss distributions, risk measures, and risk aggregation and allocation principles. A main theme is the need to satisfactorily address extreme outcomes and the dependence of key risk drivers. The techniques required derive from multivariate statistical analysis, financial time series modelling, copulas, and extreme value theory. A more technical chapter addresses credit derivatives. Based on courses taught to masters students and professionals, this book is a unique and fundamental reference that is set to become a standard in the field.

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Average Customer Review: 4 out of 5 stars

0 of 0 people found the following review helpful:

4 out of 5 stars

Very good, as far as it goes, but limited perspective, 2007-11-12
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.

1 of 1 people found the following review helpful:

5 out of 5 stars

Power tools, 2007-10-22
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.

3 of 3 people found the following review helpful:

5 out of 5 stars

Excellent for statisticians, 2007-08-03
This book is more like Mathematical Statistics for Risk Management. It covers some reviews of standard mathematical stat and some advanced and latest materials as well as applications in risk management. But as some other reviewers already mentioned, the focus is on the statistics and probability for risk management rather than the business context. And it is written in a rather formal theorem-proof format which, to some extent, could have been simplified for other audiences. It is excellent for someone with heavy stat background such as MS/PhD in statistics or PhD in Finance.

Another book that is a bit easier to read that covers Stat and Finance well with business context is: Statistics and Finance: An Introduction, which includes more than financial risk management.

5 of 5 people found the following review helpful:

4 out of 5 stars

Software implementation available for S-Plus and R, 2007-07-24
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.

2 of 2 people found the following review helpful:

3 out of 5 stars

Good for academia, bad for practice, 2007-06-08
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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