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Monte Carlo Methods in Financial Engineering: v. 53 by Paul Glasserman
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    • Product code: 16497
    • ISBN: 0387004513, ISBN13: 9780387004518, 616 pages, hardback
      Published by Springer-Verlag New York Inc. in 2003 , illustrated edition
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    Description of Monte Carlo Methods in Financial Engineering: v. 53

    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. "Mathematical 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'.

    Contents of Monte Carlo Methods in Financial Engineering: v. 53

    1. Foundations
    Principles of Monte Carlo
    Principles of Derivatives Pricing

    2. Generating Random Numbers and Random Variables
    Random Number Generation
    General Sampling Methods
    Normal Random Variables and Vectors

    3. Generating Sample Paths
    Brownian Motion
    Gaussian Short Rate Models
    Square-Root Diffusions
    Processes with Jump
    Forward Rate Models: Continuous Rates
    Forward Rate Models: Simple Rates

    4. Variance Reduction Techniques
    Control Variates
    Antithetic Variates
    Stratified Sampling
    Latin Hypercube Sampling
    Matching Underlying Assets
    Importance Sampling
    Concluding Remarks

    5. Quasi-Monte Carlo Methods
    General Principles
    Low-Discrepancy Sequences
    Lattice Rules
    Randomized QMC
    The Finance Setting
    Concluding Remarks

    6. Discretization Methods
    Introduction
    Second-Order Methods
    Extensions
    Extremes and Barrier Crossings: Brownian Interpolation
    Changing Variables
    Concluding Remarks

    7. Estimating Sensitivities
    Finite-Difference Approximations
    Pathwise Derivative Estimates
    The Likelihood Ratio Method
    Concluding Remarks

    8. Pricing American Options
    Problem Formulation
    Parametric Approximations
    Random Tree Methods
    State-Space Partitioning
    Stochastic Mesh Methods
    Regression-Based Methods and Weights
    Duality
    Concluding Remarks

    9. Applications in Risk Management
    Loss Probabilities and Value-at-Risk
    Variance Reduction Using the Delta-Gamma Approximation
    A Heavy-Tailed Setting
    Credit Risk
    Concluding Remarks

    A Appendix: Convergence and Confidence Intervals
    B Appendix: Results from Stochastic Calculus
    C Appendix: The Term Structure of Interest Rates

    References
    Index


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