Monte Carlo Methods in Finance

"Few expert practitioners also have the academic expertise to match Peter Jaeckel's in this area, let alone take the trouble to write a most accessible, comprehensive and yet self contained text. This book is a delight to read and contains a wealth of information that is essential for anyone involved with implementing Monte Carlo methods in finance." Professor Carol Alexander, ISMA Centre, University of Reading, UK

" This book is a very welcome addition to the growing literature on applied quantitative methods in finance. Dr Jaeckel has done the field a service in combining both a thorough review of 'standard' material with techniques that were learned on the job as a quant at top financial institutions. Michael Curran, Quantin' Leap

Based on the author's own experience, Monte Carlo Methods in Finance adopts a practical flavour throughout, the emphasis being on financial modelling and derivatives pricing. Numerous real world examples help the reader foster an intuitive grasp of the mathematical and numerical techniques needed to solve particular financial problems. At the same time, the book tries to give a detailed explanation of the theoretical foundations of the various methods and algorithms presented.

Monte Carlo methods have been used in the financial community for many years for addressing complex financial calculations. Recent advances by both practitioners and academic researchers in the area of fast convergence methods, together with the improvements achieved by the manufacturers of computer hardware, make Monte Carlo simulations more and more frequently the method of choice. In this long needed book on modern Monte Carlo methods in finance, Peter Jaeckel provides an introduction to many of the leading edge techniques available.

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.


Good book   May 26, 2003
5 out of 6 found this review helpful

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.


An advanced approach to math methods behind finance   September 19, 2002
9 out of 12 found this review helpful

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.


for Quants only   June 24, 2003
Franco Arda (Switzerland)
20 out of 25 found this review helpful

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.


It ain't bad, it ain't great, it ain't complete, but it ain't wrong....   March 27, 2007
Bachelier (Ile de France)
4 out of 6 found this review helpful

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.