Customer Reviews

Monte Carlo Methods in Finance

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

 

 

Conflict of interest pervades the field of finance on every level including the intellectual level.

Often you might find books reviewed most favorably by
leading academic researchers to fall far short of the praise constituting a legal liability for the author's employer and
inducing a suspicion that the reviewer had material interests at stake.

For this reason we are grateful that Amazon has a liberal return policy. This book however you will not want to send back.

It hits the mark in several important aspects:

1. Level of detail.
The presentation is detailed enough for you to be able to translate the description into computer code but not so detailed that this step is immediate.
This allows an elegant and fluent style of writing.
Descriptions that are detailed enough to translate into code immediately almost certainly lack aesthetic qualities and one usually does not read them again them once the relevant information has been extracted. This is not the case here. I find myself browsing the material again after some algorithm has already been implemented and enjoying the experience.
The author has the ability to articulate complicated concepts clearly and without resort to heavy notation.

2. Mathematical rigour.

The mathematics is impeccable. In my own experience this can be said of fewer than 10% of the books in the field of finance.
The prerequisites are minimal. You have to know the most basic properties of Brownian motion (barely more than the definition)
and be familiar with the notion of a probability density.
Nonetheless several highly interesting subjects are treated in much detail (for example effective dimensionality reduction in conjunction with the application of low discrepancy sequences).

3. Choice of subject.

The techniques discussed are those used by leading investment banks. This is unsurprising since the author himself works at such an institution. The book is quite different from one devoted to Monte Carlo methods in physics, genetics or polymer science.

4. Physical appearance.

Page size, page layout, font selection and binding are all of high quality. The book has a wealth of diagrams communicating interesting information.

I love it and believe that you will too.

You better already know the basics of Monte Carlo Simulation
to get anything out of the book.

I STRONGLY disagree with one reviewer who thinks
all one needs to know is :
1) The definition of Brownian Motion and
2) What a Probability distribution is.
FAT chance.

The book requires knowing Linear Algebra, Probability,
PDEs, Stochastic Modelling, and SDEs to be of any use.

Where's the CODE, baby!
There are very few examples put into code!

One reviewer on Amazon.com, says the book is so
detailed you don't need code. Funny, I have never
seen anything "so detailed" that an example (code)
would make the explanation less clear!

if you're a quant, you might really love this book

if you're a person who wants to have a "basic" understanding how to use MC for consulting or product pricing with examples, you got the wrong book (not mentioning that your maths must be pretty good).

if you're looking for an Excel example on how to price some basic options, i highly recommend Jackson & Staunton or Wilmott.

Very interesting and well written book reviewing more advanced mathematical concepts which might be relevant for finance engineering - not limited to Monte Carlo methods. The author seems to have a firm background in theoretical physics. Definitely not for simpletons.

I read and then reread Peter Jackel's book on Monte Carlo methods in finance, hoping to get more out of it with the extra readings. Alas, this was not the case - you can only squeeze so much juice out of a dry orange. This book looks and feels like a brain dump of a brainiac who hasn't got the ability or time to teach the foundations of the subject to his readers well. After all, if you're going to publish a book, surely you want to take a little pride in your work and what others think of it and get out of it. Very little peer review (if none at all), a rushed effort with I'm-too-good-to-really-care-about-my-readers kind of attitude were the main impressions I got out of reading this book. There are no real short cuts to learning the fairly sophisticated mathematical techniques that comprise the Monte Carlo method and the option pricing and hedging that they are being applied to. Math and engineering books on the technique, as well as papers on MC methods may be your best bet here if your aim is to really learn, understand and apply the subject with any level of confidence.

This lack of quality must also be a reflection on the publishers Wiley Finance, and I have all but given up on them running quality control on their finance books that they publish. They definitely need reminding that quality should not be sacrificed to quantity if they are to remain a viable going concern. I can only suggest that they employ qualified proof-readers in their quality control section on finance. It is this cavalier attitude to finance that got us into the mess we are now in. What does it matter if reputation doesn't matter?

This book is pretty good as it covers lots of different areas of Monte Carlo simulation and some of the newer stuffs, such as copulae, etc. The math presentation is brief but to the point as application of the mathematics to Monte Carlo methods is the emphasis. Intuitive ideas behind the formula is explained pretty well as it tells you where certain formula can be used for. It would be helpful to have taken an advanced course in Monte Carlo methods in Finance to appreciate the book. I would personally suggest Glasserman's course at Columbia U. Prof Glasserman is also writing a book on the subject that he uses for lecture notes now. It would turn out to be an even better book to read.

What this book is:
1) Dated. PJ wrote this book in 2002, using thoughts and techniques applicable to a Pentium 4 Xeon world (2001). In 2002 folks often ran option book position MC simulations *overnight.*
* Also, GOOGLE Scholar wasn't out yet....if you wanted to collect all the papers and abstracts on MC methods in 2002 you had to talk to a librarian.
2) Basic. Well, now it is basic....but when it first came out it was sharply focused on finance and it was three years ahead of Glasserman's book.
3) This book is okay for what it is, which is a topical outline, some lecture notes introducing a reasonably well math trained audience to MC and finance. In 2001 MC was a cutting edge new thing. People forget what 2001 was like: Heck, one bank was flogging that it had a 200 node binomial model programmed in Excel available for customer use on an *appointment* basis. That was the state of things at the time.
What this book is not:

1) a cookbook. There is no "cut and paste" code in here. In 2001-2 believe it or not code was made by the sweat of your brow and was considered highly proprietary. Okay so in 2007 we just cobble together Franken-code and debug, but that wasn't the way it was in 2002. There weren't "Numerical Recipes in [code flavour of the month] sites. And folks were fired for showing code ot other people.
2) It won't teach you math. You are supposed to have learned a lot of the stuff this assumes you know.
3) It won't teach you programming in [pick your language] or its step-daughters.
4) Complete. This is expanded lecture notes. Is every low discrepancy method covered? (and all its weird names) No. Is every Greek covered and every possible expression? No. Is every application covered? Hmmm, still looking for that hybrid bond model in here.....not even in the index.
5) A replacement for work. This is a "topics in" and "helpful directions" and "friendly discussion" book. It does not solve your problem on your platform for your goals. It also won't wipe your rear end, buy you beers, tuck you in at night, or let you call it "Rosie." As in Rosie fingers and Harry palm.

So what is this book good for? Well, it is a not too bad a primer, it builds your vocabulary and helps your conceptualization of goals and purposes, and if you move on to Glasserman your comprehension will be much higher, although I'm not sure he covers that much more that much better.

But if you come from a science or math background that has used MC for other purposes, and you know programming, you can probably figure out most of what PJ covers on your own.

It is a book for mathematics lovers not financial oriented profesionals. I would not recomend this book for those looking to gain more practical knowledge on this subject.