Monte Carlo Methods in Financial Engineering – Paul Glasserman
Monte Carlo Methods in Financial Engineering
# Publisher: Springer; 1 edition (August 7, 2003)
# Language: English
# ISBN-10: 0387004513
# ISBN-13: 978-0387004518
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.
Table of contents (9 chapters)
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Foundations
Pages 1-38
Glasserman, Paul
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Generating Random Numbers and Random Variables
Pages 39-77
Glasserman, Paul
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Generating Sample Paths
Pages 79-184
Glasserman, Paul
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Variance Reduction Techniques
Pages 185-279
Glasserman, Paul
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Quasi-Monte Carlo
Pages 281-337
Glasserman, Paul
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Discretization Methods
Pages 339-376
Glasserman, Paul
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Estimating Sensitivities
Pages 377-420
Glasserman, Paul
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Pricing American Options
Pages 421-479
Glasserman, Paul
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Applications in Risk Management
Pages 481-537
Glasserman, Paul
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