Customer Reviews

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

5 star:		(4)
4 star:		(2)
3 star:		(2)
2 star:		(1)
1 star:		(0)

 

 

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.

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.

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.

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.

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)

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.

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 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.

I found this book very interesting and well written, but not for all readers. It covers all quantitative risk management topics with a good mathematical approach: this could be a great textbook for general risk management courses, and it give importance to EVT technics, mulivariate models and copula-modelling. The treatment of market risk is complete and exhaustive, and a good actuarial approach for credit risk can be found too. The operational risk chapter is poor, it gives only an introductive section for it and for actuarial models.
It requires a good mathematical background to be well understand, but it is a great book to introduce the whole quantitative risk management theory.

It's a very well written self-contained yet compact "QUANTITATIVE" risk management book. Just as the title, it gives a comprehensive and in-depth coverage of the concepts, techniques and tools, applicable in modeling equity, credit, or operational risk. The writing style is clear and rigorous; it serves a good reference book.