- Series: Stochastic Modelling and Applied Probability (Book 53)
- Hardcover: 596 pages
- Publisher: Springer; 2003 edition (August 7, 2003)
- Language: English
- ISBN-10: 0387004513
- ISBN-13: 978-0387004518
- Product Dimensions: 6.1 x 1.3 x 9.2 inches
- Shipping Weight: 2.5 pounds (View shipping rates and policies)
- Average Customer Review: 4.1 out of 5 stars See all reviews (26 customer reviews)
- Amazon Best Sellers Rank: #391,921 in Books (See Top 100 in Books)
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Monte Carlo Methods in Financial Engineering (Stochastic Modelling and Applied Probability) (v. 53) 2003rd Edition
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"Paul Glasserman has written an astonishingly good book that bridges financial engineering and the Monte Carlo method. The book will appeal to graduate students, researchers, and most of all, practicing financial engineers … You will want to have prior knowledge of both the Monte Carlo method and financial engineering. If you do, you will find the book to be a goldmine … So often, financial engineering texts are very theoretical. This book is not. The Monte Carlo method serves as a unifying theme that motivates practical discussions of how to implement real models on real trading floors. You will learn plenty of financial engineering amidst these pages. The writing is a pleasure to read. Topics are timely and relevant. Glasserman's is a must-have book for financial engineers." -Glyn Holton, Contingency AnalysisMathematical 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."
From the reviews:
"This recent book is a valuable addition to the references devoted to Monte Carlo methods. … the author succeeded in choosing the most actual topics in financial engineering and in presenting them in an appropriate way by keeping a suitable balance between mathematical rigour and an audience friendly language. … To help the reader, three appendices provide basic results on convergence concepts … . A large bibliography of 358 entries accompanies this text. In short, the reader will find this book extremely lucid and useful." (Radu Theodorescu, Zentralblatt MATH, Vol. 1038 (13), 2004)
"To keep it short, let me summarize the recension in one phrase: Paul Glausserman’s book is a ‘strong buy’ for everybody in the financial community. … one gets 596 pages full of valuable information on all aspects of Monte Carlo simulation. … Altogether, I can encourage everyone interested in Monte Carlo methods in finance to read the book. It is very well written … comes with a carefully selected bibliography (358 references) and a helpful index, thus making it really worth the buy." (Ralf Werner, OR – Spectrum Operations Research Spectrum, Issue 27, 2005)
"Glasserman’s new book is a remarkable presentation of the current state of the art of Monte Carlo Methods in Financial Engineering. … lot of material which is sometimes hard to access has been composed into one volume. … a high quality monograph which is both suitable as a reference for practitioners and researchers as well as a textbook … . The list of references is by itself a valuable aspect. The refreshing writing style of the author is tailor-made for the thirsty reader … ." (Uwe Wystup, www.mathfinance.de, November, 2003)
"Paul Glasserman has written an astonishingly good book that bridges financial engineering and the Monte Carlo method. The book will appeal to graduate students, researchers, and most of all, practicing financial engineers. It is an advanced book. … The presentation is masterful. … You will learn plenty of financial engineering amidst the pages. The writing is a pleasure to read. Topics are timely and relevant. Glasserman’s is a must-have book for financial engineers." (www.riskbook.com, Dezember, 2003)
"This book is divided into three parts. … the aim of the author is … to give a precise description of the different techniques in order to facilitate their implementation. In my opinion, this book is a very comprehensive, up-to-date and useful tool for those who are interested in implementing Monte Carlo methods in a financial context." (Benjamin Jourdain, Mathematical Reviews, 2004g)
"The publication of this book is an important event in computational finance. For many years, Monte Carlo methods have been successfully applied to solve diverse problems in financial mathematics. By publishing this book the author deserves much credit for a very good attempt to lift such applications to a new level. … the book may well become a major reference in the field of applications of Monte Carlo methods in financial engineering. This is because the book is well structured and well written … ." (A Zhigljavsky, Journal of the Operational Research Society, Vol. 57, 2006)
From the Back Cover
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.
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Top Customer Reviews
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.
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.