Very well written | Monte Carlo Methods in Financial Engineering (Stochastic Modelling and Applied...

Review for Monte Carlo Methods... by P. Glasserman | Monte Carlo Methods in Financial Engineering (Stochastic Modelling and Applied Probability) | Paul Glasserman

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	• Monte Carlo Methods in Financial Engineering (Stochastic Modelling and Applied Probability) Paul Glasserman Springer, 2003 - 602 pages average customer review:based on 20 reviews view larger image
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highly recommended

Best financial engineering book on MC

This is like the bible of Monte Carlo methods in financing. Both a good read and a good reference book. Must have! for any quant on wall street.

Very well written

I must admit it is magnificently written book. Not just the main thematical issues but also appendix, where, compared to other similar publications, Girsanov theorem is thoroughly explained. All in all it's
a very useful book for everyone who has some basic information about stochastic processes and wants to learn something more about it or deepen their's knowledges. Well done.

Review for Monte Carlo Methods... by P. Glasserman

The book is just right for a reader who is looking for state-of-the-art techniques in Monte-Carlo methods in general. The fact that the book is specific to financial systems does not limit the usability of the book in the manner it is written. There are a lots of useful references one can get out of this book.
The book is for advanced readers in the sense that it requires rigorous mathematical ability to understand all the concepts. It is by no means for a novice reader and requires background in computational mathematics.

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Advanced analysis

Let me start by saying that I'm not a "quant." I am interested in the calculations that quants do, and in Monte Carlo techniques in general. As a result, I'm reviewing only about half of this book, the half on generally applicable Monte Carlo techniques, and skipping the finance-specific material that it alternates with.

As something of a novice to advanced Monte Carlo techniques, I find this book immensely useful. The chapter on "Generating Random Numbers" helps, even if the description of the basic uniform generators could be stronger. Given the uniform generator, its descriptions of generators for non-uniform distributions work well for me. The "Sample Path" material is where I came into this book, really, looking for more insight into generation Brownian bridges. The math certainly is not for the notation-shy, but suffices for the dedicated practitioner. The next few chapters on variance reduction, quasi-MC, discretization, and sensitivity analysis are all widely applicable - I don't have immediate use for the material, but now I know where to look when the need arises. The remaining two chapters cover specific financial applications, and I leave comment on them to other readers.

This book gave me what I wanted, and lots more besides. Much of what it offers really isn't for me, though - the financial instruments being analyzed border on abstract art. I also felt a little pain at having no background in stochastic calculus, but some determination and a willingness to skip over fine points got me through well enough. The successful reader has a working knowledge of basic calculus, linear algebra, and probability. That reader must have a real interest in MC techniques, and should care about the financial decision-making to which Glasserman applies those techniques - but, as I prove, even that isn't necessary for getting a lot of value from this text.

-- wiredweird

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good book on Monte Carlo in Finance

But it seems the author is a little focused on selling his ideas, but not a very subjective overview of all topics in M-C method in finance.

Monte Carlo simulation has become an essential tool in the pricing of derivative securities and in risk management. These applications have, in turn, stimulated research into new Monte Carlo methods and renewed interest in some older techniques.

This book develops the use of Monte Carlo methods in finance and it also uses simulation as a vehicle for presenting models and ideas from financial engineering. It divides roughly into three parts. The first part develops the fundamentals of Monte Carlo methods, the foundations of derivatives pricing, and the implementation of several of the most important models used in financial engineering. The next part describes techniques for improving simulation accuracy and efficiency. The final third of the book addresses special topics: estimating price sensitivities, valuing American options, and measuring market risk and credit risk in financial portfolios.

The most important prerequisite is familiarity with the mathematical tools used to specify and analyze continuous-time models in finance, in particular the key ideas of stochastic calculus. Prior exposure to the basic principles of option pricing is useful but not essential.

The book is aimed at graduate students in financial engineering, researchers in Monte Carlo simulation, and practitioners implementing models in industry.

Mathematical Reviews, 2004: "... this book is very comprehensive, up-to-date and useful tool for those who are interested in implementing Monte Carlo methods in a financial context."

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