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Monte Carlo Methods in Financial Engineering (Stochastic Modelling and Applied Probability) (v. 53) Hardcover – August 7, 2003

ISBN-13: 978-0387004518 ISBN-10: 0387004513 Edition: 2003rd

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Monte Carlo Methods in Financial Engineering (Stochastic Modelling and Applied Probability) (v. 53) + Stochastic Calculus for Finance I: The Binomial Asset Pricing Model (Springer Finance / Springer Finance Textbooks) + Stochastic Calculus for Finance II: Continuous-Time Models (Springer Finance / Springer Finance Textbooks)
Price for all three: $181.00

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Product Details

  • 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: 4.0 out of 5 stars  See all reviews (21 customer reviews)
  • Amazon Best Sellers Rank: #262,706 in Books (See Top 100 in Books)

Editorial Reviews

Review

"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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Customer Reviews

This is like the bible of Monte Carlo methods in financing.
Lijun Shi
For readers that are interested in really understanding Monte-Carlo and sophisticated analytical techniques from a very indepth standpoint, this book is for you.
Kindle Customer
I just got this book and start reading a few topics of interest like Risk Management.
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Most Helpful Customer Reviews

82 of 86 people found the following review helpful By Rama CONT on July 30, 2004
Format: Hardcover
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.
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28 of 33 people found the following review helpful By Dr. Lee D. Carlson HALL OF FAMEVINE VOICE on July 8, 2005
Format: Hardcover Verified Purchase
Monte Carlo simulations are extensively used not only in finance but also in network modeling, bioinformatics, radiation therapy planning, physics, and meteorology, to name a few. This book gives a good overview of how they are used in financial engineering, with particular emphasis on pricing American options and risk management. Aspiring financial engineers will find much that is helpful in the book, and after reading it should be able to apply the methodologies in the book in whatever financial institution they find themselves employed. The mathematics may be too formidable for a practical trader, but the book is targeted to readers who intend to work as financial engineers in a high-powered financial institution. Due to constraints of space, only the last two chapters will be reviewed here.

The next-to-last chapter discusses the difficult problem of pricing American options, which the author introduces as an `embedded optimization problem': the value of an American option is found by finding the optimal expected discounted payoff, in order to find the best time to exercise the option. When applying Monte Carlo simulation, the author restricts himself to options that can only be exercised at a finite, fixed set of opportunities, with a discrete Markov chain used to model the underlying process representing the discounted payoff from the exercise of the option at a particular time. This allows the use of dynamic programming, which the author does throughout the chapter, with the further simplification that the discounting is omitted. The author also shows how to find the optimal value by finding the best value within a parametric class, giving in the process a more tractable problem. This approach considers a parametric class of exercise regions or stopping rules.
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10 of 10 people found the following review helpful By wiredweird HALL OF FAMETOP 500 REVIEWER on January 4, 2008
Format: Hardcover Verified Purchase
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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