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Quantitative Methods in Derivatives Pricing: An Introduction to Computational Finance
 
 
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Quantitative Methods in Derivatives Pricing: An Introduction to Computational Finance [Hardcover]

Domingo Tavella (Author)
3.2 out of 5 stars  See all reviews (8 customer reviews)

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Book Description

Wiley Finance April 19, 2002
This book presents a cogent description of the main methodologies used in derivatives pricing. Starting with a summary of the elements of Stochastic Calculus, Quantitative Methods in Derivatives Pricing develops the fundamental tools of financial engineering, such as scenario generation, simulation for European instruments, simulation for American instruments, and finite differences in an intuitive and practical manner, with an abundance of practical examples and case studies. Intended primarily as an introductory graduate textbook in computational finance, this book will also serve as a reference for practitioners seeking basic information on alternative pricing methodologies.

Domingo Tavella is President of Octanti Associates, a consulting firm in risk management and financial systems design. He is the founder and chief editor of the Journal of Computational Finance and has pioneered the application of advanced numerical techniques in pricing and risk analysis in the financial and insurance industries. Tavella coauthored Pricing Financial Instruments: The Finite Difference Method. He holds a PhD in aeronautical engineering from Stanford University and an MBA in finance from the University of California at Berkeley.


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Editorial Reviews

From the Inside Flap

Quantitative Methods in Derivatives Pricing

Quantitative Methods in Derivatives Pricing, researched and written by Domingo Tavella, one of the pioneers in the emergence of computational finance as a discipline in its own right, develops the main techniques and strategies of computational finance in a unified framework. From the plethora of methods that characterize a new discipline in a state of fluid evolution, this book concentrates on those that have proven to be sufficiently solid and robust to become a permanent part of the arsenal of strategies for pricing complex financial instruments. Either as a textbook or a reference source, this book's emphasis is on practicality and applications.

As a textbook, this work fills a palpable need for adequate material in the ever-increasing number of programs with an emphasis on sophisticated financial engineering. As a reference source, it provides a valuable overview of the most relevant methods and approaches of computational finance for those with adequate quantitative background entering the field of financial pricing. Topics discussed include:
* A brief introduction to single-period pricing
* A self-contained, practical introduction to stochastic calculus, with an emphasis on practical applications
* Introduction to continuous-time pricing
* Generation of scenarios for simulation, discussing methods and accuracy in detail
* Simulation applied to computing expectations for European pricing
* Simulation applied to early exercise pricing, presenting a detailed description of the least squares Monte Carlo method
* The use of finite differences in option pricing

Filled with numerous case studies and expert advice, Quantitative Methods in Derivatives Pricing offers the most complete look at proven computational techniques for derivatives pricing to date. You'll quickly learn about the pricing of derivatives in continuous time, how asset-pricing theory is used to set up the pricing problem, and how to implement simulation methods for pricing both European and early exercise derivatives.

This invaluable resource prepares you for the rigors of the modern financial world and puts you on the road to successful derivatives pricing.

From the Back Cover

Praise for Quantitative Methods in Derivatives Pricing

"Tavella's text is ideal for a course on computational methods in finance. I cannot think of a better book for the purpose. The writing is clear and intuitive. The marriage of mathematical methods and financial applications is just right for a first course on the topic, especially with the excellent working examples for Monte Carlo and finite-difference methods."
-Darrell Duffie, Professor of Finance
Stanford University

"This is a masterful and detailed survey of the fundamental tools and techniques available to financial engineers."
-Francis Longstaff, Professor of Finance, UCLA

"Quantitative Methods in Derivatives Pricing is a valuable addition to the books available to the beginning graduate student or practitioner. As well as containing a nice treatment of the theoretical principles of modern financial derivatives, it is the first to stress the fundamentals of the wide variety of computational algorithms used for pricing and hedging. Unlike many of its competitors, it is succinct and clearly written."
-M. A. H. Dempster, Professor of Finance and Director
Centre for Financial Research, Cambridge University

"This textbook provides a superb introduction to quantitative derivative pricing techniques that is a must read for MFE students. Domingo Tavella develops a uniform framework for derivative valuation in terms of computing expectations. He then analyzes the pricing theory and practice using simulation and finite differences. Readers will find unique insights into implementation issues associated with these state-of-the-art pricing techniques."
-Joshua Rosenberg, Associate Editor, Journal of Computational Finance

Product Details

  • Hardcover: 256 pages
  • Publisher: Wiley; 1st edition (April 19, 2002)
  • Language: English
  • ISBN-10: 0471394475
  • ISBN-13: 978-0471394471
  • Product Dimensions: 9.7 x 6.6 x 1 inches
  • Shipping Weight: 1.3 pounds (View shipping rates and policies)
  • Average Customer Review: 3.2 out of 5 stars  See all reviews (8 customer reviews)
  • Amazon Best Sellers Rank: #1,130,772 in Books (See Top 100 in Books)

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Customer Reviews

8 Reviews
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 (4)
4 star:    (0)
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Average Customer Review
3.2 out of 5 stars (8 customer reviews)
 
 
 
 
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Most Helpful Customer Reviews

18 of 20 people found the following review helpful:
2.0 out of 5 stars A book for the mathematically inclined, July 14, 2002
By 
This review is from: Quantitative Methods in Derivatives Pricing: An Introduction to Computational Finance (Hardcover)
The book covers pricing of derivatives and the underlying computational methods. This broad range of topics covers aspects like stochastic calculus, risk neutral pricing and computational methods. The communication of this broad range of topics is a challenge and the book might be fine tuned to better teach the reader besides the intuition of the methods, the detailed implementation. It is suitable for people with a very strong mathematics and programming background, but is a tough read if one wants to learn these subjects. In order to become a good how -to book, the examples provided need to be expanded and ideally worked out in a more detailed fashion. One great add on might be to have a disk with sample code, that shows how the different methods work and how to implement them.

Positive is:
- Good section on stochastic calculus
- Good introduction to risk free pricing

Areas for improvement
- Expand examples
- Better quality check to avoid typos, that are especially annoying in formulas
- If this book is to be used as a textbook or for self study, practice examples with solutions would be great, as the reader can then work through these to internalize the material and in addition check if he has fully understood the material

Overall I can only recommend the book to people with strong liking of a mathematical treatment of a subject, strong programming skills and little need for detailed examples. It does not go into sufficient detail on how to implement the different simulation strategies into code (provides only "pseudo code") to teach the computational aspects.

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10 of 16 people found the following review helpful:
5.0 out of 5 stars Excellent Reference for Computational Finance, August 9, 2002
By A Customer
This review is from: Quantitative Methods in Derivatives Pricing: An Introduction to Computational Finance (Hardcover)
This is an excellent introduction book on computational finance. It covers Monte Carlo simulation for pricing and scenario generations and finite difference methods very well. I really like the part on Monte Carlo simulation with various variance reduction techniques such as Brownian Bridge.

The author not only presents the methodologies, but he also tells the readers their limitations. This book is also a good resource for basics of stochastic processes most commonly needed in practice. I think the book is beneficial both to practitioners and students who really wants to consider financial engineering as a career.

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4 of 7 people found the following review helpful:
2.0 out of 5 stars sloppy editing, little additional material over previous work, October 23, 2007
This review is from: Quantitative Methods in Derivatives Pricing: An Introduction to Computational Finance (Hardcover)
Slapped together and little better than his original book. The original and a few downloaded PDFs are a better value. I'm beginning to think JW&S specializes in adding slipcovers with fancy graphics and nice new clean typefaces onto stale old previous material. Like middle-aged men who suddenly start dressing bizarrely younger in an unsuitable style, the result is neither value added, becoming, or informative.
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Inside This Book (learn more)
First Sentence:
The purpose of this short chapter is to motivate the notion that the price of a financial instrument is expressed in the form of an expectation of suitably discounted future values or cash flows. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
normalizing asset, uncontrolled estimator, jth trajectory, discretization matrix, antithetic method, displacement shocks, risk neutral measure, pricing equation, derivatives pricing, control variate, financial pricing, optimal exercise strategy, numeraire asset, continuation value, exercise boundary, computational barrier, martingale representation theorem, default intensity, time discretization scheme, early exercise features, drift adjustment, basic random variables, scenario generation, barrier option, exercise boundaries
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Fundamentals of Stochastic Calculus, Replacing Equations, Smax Smin
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