蒙特卡洛模拟和金融MONTE CARLO SIMULATION AND FINANCE
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作者： Don L. McLeish著
出 版 社： 吉林长白山
出版时间： 2005-12-1字数：版次： 1页数： 387印刷时间： 2005/12/01开本：印次：纸张： 胶版纸I S B N ： 9780471677789包装： 精装编辑推荐
作者简介：
DON L. McLEISH is Professor of Statistics and Actuarial Science at the University of Waterloo. His research has focused on probability, statistical methods and models in general, and their application to financial data, including wide-tail alternatives to the normal distribution and the consequences for derivatives and asset pricing. He has contributed to the application of Monte Carlo techniques, variance reduction, and stochastic calculus to problems in finance, and is cofounder of the University of Waterloo's Center for Advance Studies in Finance. McLeish is also coauthor, with C.G. Small, of The Theory and Application of Statistical Inference Functions and Hilbert Space Methods in Probability and Statistical Inference (Wiley).
内容简介
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.
This state-of-the-art book on Monte Carlo simulation methods is ideal for finance professionals and students. Order your copy today.
目录
Acknowledgments
Chapter 1 Introduction
Chapter 2 Some Basic Theory of Finance
Introduction to Pricing: Single PeriodModels
Multiperiod Models
Determining the Process Bt
Minimum Variance Portfolios and the Capital Asset Pricing Model
Entropy: choosing a Q measure
Models in Continuous Time
Problems
Chapter 3 Basic Monte Carlo Methods
Uniform Random Number Generation
Apparent Randomness of Pseudo-Random Number Generators
Generating Random Numbers from Non-Uniform Continuous Distributions
Generating Random Numbers from Discrete Distributions
Random Samples Associated with Markov Chains
Simulating Stochastic Partial Differential Equations
Problems
Chapter 4 Variance Reduction Techniques
Introduction
Variance reduction for one-dimensional Monte-Carlo Integration
Problems
Chapter 5 Simulating the value of Options
Asian Options
Pricing a Call option under stochastic interest rates
Simulating Barrier and lookback options
Survivorship Bias
Problems
Chapter 6 Quasi- Monte Carlo Multiple Integration
Introduction
Theory of Low discrepancy sequences
Examples of low discrepancy sequences
Problems
Chapter 7 Estimation and Calibration
Introduction
Finding a Root
Maximization of Functions
MaximumLikelihood Estimation
Using Historical Data to estimate the parameters in Diffusion Models
Estimating Volatility
Estimating Hedge ratios and Correlation Coefficients
Problems
Chapter 8 Sensitivity Analysis, Estimating Derivatives and the Greeks
Estimating Derivatives
Infinitesimal Perturbation Analysis: Pathwise differentiation
Calibrating aModel using simulations
Problems
Chapter 9 Other Directions and Conclusions
Alternative Models
ARCH and GARCH
Conclusions
Notes
References
Index
