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Rating: 
Summary: Mathematically disappointing book
Review: Don't be fooled by the name of the Springer series where this book appeared: if you are looking for a nice list of examples and applications, then this book may be ok, but otherwise, this is not the place to look for proofs and rigorous results.
Rating:
Summary: Mathematically disappointing book
Review: I just got this book and start reading a few topics of interest like Risk Management. The book covers a lot of material in various financial products (heavy on interest rate products) and disciplines and does a fairly detailed job. It would have been great to have expanded the book to cover some areas more in depth (credit and operational risk), but otherwise this book is pretty comprehensive in terms of Monte Carlo applications. The book also has a nice appendix section that covers stochastic calculus and other topics. I took a course by Professor Glasserman at Columbia University ages ago and the book as well as the course delivers. This book is an excellent reference for any practitioner or academic alike (highly recommended). If you had to choose, I also think this book is better than the Peter Jaeckel's book on Monte Carlo. Enjoy...
Rating: 
Summary: Monte Carlo applications and much more!
Review: I just got this book and start reading a few topics of interest like Risk Management. The book covers a lot of material in various financial products (heavy on interest rate products) and disciplines and does a fairly detailed job. It would have been great to have expanded the book to cover some areas more in depth (credit and operational risk), but otherwise this book is pretty comprehensive in terms of Monte Carlo applications. The book also has a nice appendix section that covers stochastic calculus and other topics. I took a course by Professor Glasserman at Columbia University ages ago and the book as well as the course delivers. This book is an excellent reference for any practitioner or academic alike (highly recommended). If you had to choose, I also think this book is better than the Peter Jaeckel's book on Monte Carlo. Enjoy...
Rating:
Summary: Compared to the best, this is average.
Review: This book has a good explanation of Monte Carlo methods, but so do many others. Given that the focus of this book is interest rate models, I must compare it with the best in the field, and this book falls short. The definitive encyclopedia is "Interest Rate Modelling: Financial Engineering" by Jessica James and Nick Webber. Ms. James's Ph.D. in physics and on-line experience shows through in the sound explanation and application of theory.
Glasserman falls down in the actual applications, since some of the key real-world ingredients such as day counts and quirks of the market are missing.
"Interest Rate Modelling" covers these features and more. It also reviews hundreds of publications. All the methods for term structure modeling are clearly discussed, and the authors made improvements on some of the original works. "Interest Rate Modelling" still the standard for serious professionals, and while this book is good, compared to a superior work it only merits 2.5 stars.
Rating:
Summary: a great buy
Review: This is the best book I've read in the last year on mathematical finance. It is a tightly focussed text on Monte Carlo methods no more no less. So you won't find things like day count fracs because that's not what it's about. Glasserman is a true expert on the topic. My highlight was the chapter on variance reduction where the vast amount of detailed knowledge taught me a lot, although I implement monte carlo pricing models on a day to day basis.
Rating: 
Summary: An accessible overview of Monte Carlo methods in finance
Review: This new book, written by an active contributor to the field of Monte Carlo methods in finance, summarizes the ongoing interaction between theory and practice in a way that is readily accessible to graduate students and practitioners in quantitative finance.
The book is as self-contained as possible: basic notions on Monte Carlo simulation and option pricing are recalled in the first chapter and the second chapter explains how random number generators are designed. Chapter 3 explains how to generate sample paths for some commonly used stochastic models: multifactor Gaussian models, square root diffusions, diffusions with Poisson jumps, some examples of Lévy processes and the LIBOR market model. Instead of giving a general result and leaving the reader on his own, the author treats each example with a fair amount of detail.
Chapter 4, which is the longest and probably the best chapter in the book, discusses variance reduction techniques. Variance reduction is what makes all the difference between a basic Monte Carlo simulation and a state-of-the-art algorithm incorporating the tricks of the trade. Apart from classical topics such as control variates, stratified sampling and importance sampling, the author (briefly) discusses more advanced topics such as the Weighted Monte Carlo method of Avellaneda et al., viewing it as a variance reduction method.
While computation of prices as expectations are standard applications of the Monte Carlo methods, two other issues in finance have turned out to be more challenging to solve using Monte Carlo simulation: the computation of sensitivities ("Greeks") and the pricing of American options, which involves the maximization of conditional expectations. Chapter 7 deals with the computation of sensitivities using finite differences, pathwise derivatives and the likelihood ratio method. More advanced methods based on integration by parts ("Malliavin calculus") are only briefly mentioned in the conclusion to this chapter.
Chapter 8 deals with the (Monte Carlo) pricing of American options, an evolving research topic in which Paul Glasserman has been an active contributor. The author has succeeded in summarizing in 60 pages a survey of various approaches: parametric methods, quantization methods, the (Broadie-Glasserman) stochastic mesh method, regression-based methods of Carriere-Longstaff-Schwartz and duality methods (Haugh-Kogan, Rogers). The presentation is somewhat biased towards the Broadie-Glasserman approach (which is understandable..), whereas the Carrière-Longstaff-Schwartz regression method seems to be the most popular one among practitioners. One can regret the absence of a systematic comparison between these various methods in terms of numerical performance but the chapter explains their interrelations, at least from a theoretical point of view.
While most texts on Monte Carlo methods in finance have exclusively focused on option pricing, simulation of extreme events in view of VaR computation constitute another important application of Monte Carlo simulation. Chapter 9 deals with this topic and presents some importance sampling methods for simulating tail events, which turn out to be especially useful when simulating joint default events in credit risk models. A crash course on credit risk modeling is included in the chapter.
The book is not written in a theorem-proof format but using an explanatory approach which I found quite pleasant, with lots of examples illustrating the results. This format seems suitable for students of financial engineering; mathematicians looking for proofs of convergence should look elsewhere. The level of generality of the results is just right for applications in finance: the author has avoided the pitfall of considering a too general framework and has chosen to focus on examples of stochastic processes actually used in financial engineering, which makes the text more understandable. Also, various simulation methods are compared by actually doing the simulations instead of simply discussing asymptotic convergence rates. What is lacking is perhaps a more systematic reference to bibliography to indicate where proofs of various results are to be found, which could be useful for PhD students or researchers consulting this book.
One can always complain about topics which have been left out or lightly treated- weighted Monte Carlo, parallel computing, Malliavin calculus, quantization methods, point processes, LIBOR models with jumps,...-but the book is already 600 pages long and it seems retrospectively that it would have been difficult to include more material without greatly expanding the volume.
I have no doubt that this book will find many interested readers among quants and graduate students in quantitative finance and can even serve as an introduction to quantitative finance for non-specialist readers with a good quantitative background.
Rating:
Summary: Excellent Read
Review: Very well written book , all you need to know about MC Methods.
If you want to buy one book buy this one, if you have deep pockets then may be you should get the Peter Jaeckal book along with this. There is another introductory book on Simulation by Sheldon Ross.
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