[下载]Peter Jaeckel: Monte Carlo Methods in Finance

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<P><B><FONT size=3><a href="http://www.amazon.com/gp/reader/047149741X/ref=sib_dp_pt/002-9097004-5288068#reader-link" target="_blank" ><IMG src="http://images.amazon.com/images/P/047149741X.01._BO2,204,203,200_PIsitb-dp-500-arrow,TopRight,45,-64_AA240_SH20_SCLZZZZZZZ_.jpg" border=0></A></FONT></B></P>
<P><B><FONT size=3>Monte Carlo Methods in Finance (Hardcover)
</FONT></B>by <a href="http://www.amazon.com/exec/obidos/search-handle-url/index=books&field-author-exact=Peter%20Jaeckel&rank=-relevance%2C%2Bavailability%2C-daterank/002-9097004-5288068" target="_blank" ><FONT color=#003399>Peter Jaeckel</FONT></A></P>
<P><B><FONT color=#cc6600 size=3>Editorial Reviews
</FONT></B></P>
<DIV class=content><B>Book Description</B>
An invaluable resource for quantitative analysts who need to run models that assist in option pricing and risk management. This concise, practical hands on guide to Monte Carlo simulation introduces standard and advanced methods to the increasing complexity of derivatives portfolios. Ranging from pricing more complex derivatives, such as American and Asian options, to measuring Value at Risk, or modelling complex market dynamics, simulation is the only method general enough to capture the complexity and Monte Carlo simulation is the best pricing and risk management method available.
The book is packed with numerous examples using real world data and is supplied with a CD to aid in the use of the examples.

<B>Book Info</B>
This text adopts a practical flavor throughout, the emphasis being on financial modeling and derivatives pricing. Provides a detailed explanation of the theoretical foundations of the various methods and algorithms presented.
</DIV>
<UL>
<LI>
<DIV class=content><B>Publisher:</B> John Wiley & Sons; Book & CD edition </DIV></LI>
<LI>
<DIV class=content><B>Language:</B> English</DIV></LI>
<LI>
<DIV class=content><B>ISBN:</B> 047149741X </DIV></LI>
<LI>
<DIV class=content>2002</DIV></LI>
<LI>
<DIV class=content>232 Pages, </DIV></LI>
<LI>
<DIV class=content>PDF</DIV></LI>
<LI>
<DIV class=content>24.3MB</DIV></LI></UL>
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2005-9-19 03:22:00 上传

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<P>--------------------------------------------------------------------</P>
<P align=center><FONT color=#e6421a size=4>Peter Jaeckel: Monte Carlo Methods in Finance</FONT></P>
<P>(Official, 2002, 232 Pages, DJUV, 5.59MB, Wiley)</P>
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[此贴子已经被作者于2005-9-19 11:20:23编辑过]

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关键词：Monte Carlo Finance Jaeckel Methods Method complexity management practical standard methods

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[此贴子已经被作者于2005-9-17 23:15:22编辑过]

A Primer for the Monte Carlo Method (Paperback) by Ilya M. Sobol

Book Description The Monte Carlo method is a numerical method of solving mathematical problems through random sampling. As a universal numerical technique, the method became possible only with the advent of computers, and its application continues to expand with each new computer generation. A Primer for the Monte Carlo Method demonstrates how practical problems in science, industry, and trade can be solved using this method. The book features the main schemes of the Monte Carlo method and presents various examples of its application, including queueing, quality and reliability estimations, neutron transport, astrophysics, and numerical analysis. The only prerequisite to using the book is an understanding of elementary calculus.

Product Details

• Paperback: 107 pages
• Publisher: CRC-Press; 1 edition (May 19, 1994)
• Language: English
• ISBN: 084938673X

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Monte Carlo Simulation and Finance

by Don L. McLeish

Description of Monte Carlo Simulation and Finance

A state-of-the-art book on Monte Carlo simulation methods for finance professionals and students Monte Carlo methods have been used for decades in physics, engineering, statistics, and other fields. Monte Carlo Simulation and Finance explains the nuts and bolts of this essential technique used to value derivatives and other securities. Author and educator Don McLeish examines this fundamental process, and discusses important issues, including specialized problems in finance that Monte Carlo and Quasi-Monte Carlo methods can help solve and the different ways Monte Carlo methods can be improved upon.

About Don L. McLeish

Don L. McLeish (Ontario, Canada) is Professor of Statistics and Actuarial Science at the University of Waterloo. His research has focused on statistical models for financial data, including the application of wide-tail alternatives to the normal distribution such as stable processes, and the consequences for derivatives and asset pricing.

290 Pages,

Published by John Wiley & Sons,

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[此贴子已经被作者于2005-9-18 23:01:43编辑过]

Monte Carlo Methods in Financial Engineering

by Paul Glasserman

Our price: £38.48 Product code: 16497, ISBN: 0387004513, 600 pages, hardback, published by Springer Verlag, 1st edition, 2003 Description of Monte Carlo Methods in Financial Engineering Monte Carlo Methods are among the most broadly applicable and thus most powerful tools for valuing derivatives securities and measuring their risks. As computer speeds continue to increase and new research expands the scope and efficiency of these methods, their use is destined to grow. This book is devoted to the use of Monte Carlo methods in finance. Advances in Monte Carlo methods in financial engineering take place at the interface between academic research and industry practice. This book targets that interface developing theory closely tied to applications. It is roughly divided into three parts: the first three chapters concentrate on the basics of Monte Carlo methods; the next three develop ways to improve Monte Carlo methods; and the final four chapters deal with more specialized problems arising, in particular applications of Monte Carlo to financial engineering. This book will serve as a reference for practitioners and researchers and will also be suitable as a graduate text for courses on computational finance. Contents of Monte Carlo Methods in Financial Engineering 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

[此贴子已经被作者于2005-9-18 3:21:18编辑过]

Monte Carlo Statistical Methods

by Christian P. Robert and George Casella

Our price: £50.58 + postage

Description of Monte Carlo Statistical Methods

Until the advent of powerful and accessible computing methods, the experimenter was often confronted with a difficult choice. Either describe an accurate model of a phenomenon, which would usually preclude the computation of explicit answers, or choose a standard model which would allow this computation, but may not be a close representation of a realistic model. This dilemma is present in many branches of statistical applications, for example in electrical engineering, aeronautics, biology, networks, and astronomy. Markov chain Monte Carlo methods have been developed to provide realistic models.

Contents of Monte Carlo Statistical Methods

1 Introduction Statistical Models Likelihood Methods Bayesian Methods Deterministic Numerical Methods Problems Notes 2 Random Variable Generation Basic Methods - Introduction - The Kiss Generator - Beyond Uniform Distributions Transformation Methods Accept-Reject Methods - General Principle - Envelope Accept-Reject Methods - Log-Concave Problems Notes 3 Monte Carlo Integration Introduction Classical Monte Carlo integration Importance Sampling - Principles - Finite Variance Estimators - Comparing Importance Sampling with Accept-Reject Riemann Approximations Laplace Approximations The Saddlepoint Approximation - An Edgeworth Derivation - Tail Areas Acceleration Methods - Antithetic Variables - Control Variates - Conditional Expectations Problems Notes 4 Markov Chains Essentials for MCMC Basic notions Irreducibility, atoms and small sets - Irreducibility - Atoms and small sets - Cycles and Aperiodicity Transience and Recurrence - Classification of irreducible chains - Criteria for Recurrence - Harris Recurrence Invariant Measures Ergodicity and convergence - Ergodicity - Geometric convergence - Uniform ergodicity Limit theorems - Ergodic theorems - Central limit theorems Covariance in Markov Chains Problems Notes 5 Monte Carlo Optimization Introduction Stochastic Exploration - A basic solution - Gradient Methods - Simulated Annealing - Prior Feedback Stochastic Approximation - Missing data models and demarginalization - Monte Carlo Approximation - The EM Algorithm - Monte Carlo EM Problems Notes 6 The Metropolis-Hastings Algorithm Monte Carlo Methods based on Markov Chains The Metropolis-Hastings algorithm - Definition - Convergence Properties A Collection of Metropolis-Hastings Algorithms - The Independent Case - Random Walks - ARMS: A General Metropolis-Hastings Algorithm Optimization and Control - Optimizing and Acceptance Rate - Conditioning and Acceleration Further Topics - Reversible Jumps - Langevin algorithms Problems Notes 7 The Gibbs Sampler General Principles - Definition - Completion - Convergence Properties - Gibbs sampling and Metropolis-Hastings - The Hammersley-Clifford Theorem - Hierarchical Structures The Two-Stage Gibbs Sampler - Dual Probability Structures - Reversible and Interleaving Chains - Monotone Covariance and Rao-Blackwellization - The Duality Principle Hybrid Gibbs samplers - Comparison with Metropolis-Hastings Algorithms - Mixtures and Cycles - Metropolizing the Gibbs sampler - Reparameterization Improper Priors Problems Notes 8 Diagnosing Convergence Stopping the Chain - Convergence Criteria - Multiple Chains - Conclusions Monitoring Convergence to the Stationary Distribution - Graphical Methods - Nonparametric tests of stationarity - Renewal Methods - Distance evaluations Monitoring Convergence of Averages - Graphical Methods - Multiple Estimates - Renewal Theory - Within and between variances Simultaneous Monitoring - Binary control - Valid Discretization Problems Notes 9 Implementation in Missing Data Models Introduction First examples - Discrete Data Models - Data missing at random Finite mixtures of distributions A Reparameterization of Mixtures Extensions - Hidden Markov chains - Changepoint models - Stochastic Volatility Problems Notes A Probability Distributions B Notation Mathematical Probability Distributions Markov Chains Statistics Algorithms C References Author Index Subject Index

[此贴子已经被作者于2005-9-18 3:22:04编辑过]

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