金融工程中的蒙特卡罗方法（影印版）
点此进入淘宝搜索页搜索分類: 图书,管理,金融/投资,金融理论,
作者： （美）格拉瑟曼 著
出 版 社： 高等教育出版社
出版时间： 2008-6-1字数： 800000版次： 1页数： 596印刷时间： 2008-6-1开本： 16开印次： 1纸张： 胶版纸I S B N ： 9787040247527包装： 平装
本书中介绍了蒙特卡罗方法在金融中的用途，并且将模拟用作呈现金融工程中模型和思想的工具。本书大致分为三个部分。第一部分介绍了蒙特卡罗方法的基本原理，衍生定价基础以及金融工程中一些最重要模型的实现。第二部分描述了如何改进模拟精确度和效率。最后的第三部分讲述了几个特别的论题：价格敏感度估计，美式期权定价以及金融投资组合中的市场风险和信贷风险评估。
Paul Glasserman，哥伦比亚大学商学院高级副院长、Jack R．Anderson教授，美国联邦储蓄保险公司(FDIC)金融研究中心成员。长期从事风险管理、衍生证券定价、Monte Carlo模拟等方向的教学和研究，曾发表许多有影响力的研究论文，并担任著名刊物Management Science、Finance&Stochastics、Mathematical Finance等的编委。
1 Foundations
1.1 Principles of Monte Carlo
1.1.1 Introduction
1.1.2 First Examples
1.1.3 Efficiency of Simulation Estimators
1.2 Principles of Derivatives Pricing
1.2.1 Pricing and Replication
1.2.2 Arbitrage and Risk-Neutral Pricing
1.2.3 Change of Numeraire
1.2.4 The Market Price of Risk
2 Generating Random Numbers and Random Variables
2.1 Random Number Generation
2.1.1 General Considerations
2.1.2 Linear Congruential Generators
2.1.3 Implementation of Linear Congruential Generators
2.1.4 Lattice Structure
2.1.5 Combined Generators and Other Methods
2.2 General Sampling Methods
2.2.1 Inverse Transform Method
2.2.2 Acceptance-Rejection Method
2.3 Normal Random Variables and Vectors
2.3.1 Basic Properties
2.3.2 Generating Univariate Normals
2.3.3 Generating Multivariate Normals
3 Generating Sample Paths
3.1 Brownian Motion
3.1.1 One Dimension
3.1.2 Multiple Dimensions
3.2 Geometric Brownian Motion
3.2.1 Basic Properties
3.2.2 Path-Dependent Options
3.2.3 Multiple Dimensions
3.3 Gaussian Short Rate Models
3.3.1 Basic Models and Simulation
3.3.2 Bond Prices
3.3 Multifactor Models
3.4 Square-Root Diffusions
3.4.1 Transition Density
3.4.2 Sampling Gamma and Poisson
3.4.3 Bond Prices
3.4.4 Extensions
3.5 Processes with Jumps
3.5.1 A Jump-Diffusion Model
3.5.2 Pure-Jump Processes
3.6 Forward Rate Models: Continuous Rates
3.6.1 The HJM Framework
3.6.2 The Discrete Drift
3.6.3 Implementation
3.7 Forward Rate Models: Simple Rates
3.7.1 LIBOR Market Model Dynamics
3.7.2 Pricing Derivatives
3.7.3 Simulation
3.7.4 Volatility Structure and Calibration
4 Variance Reduction Techniques
4.1 Control Variates
4.1.1 Method and Examples
4.1.2 Multiple Controls
4.1.3 Small-Sample Issues
4.1.4 Nonlinear Controls
4.2 Antithetic Variates
4.3 Stratified Sampling
4.3.1 Method and Examples
4.3.2 Applications
4.3.3 Poststratification
4.4 Latin Hypercube Sampling
4.5 Matching Underlying Assets
4.5.1 Moment Matching Through Path Adjustments
4.5.2 Weighted Monte Carlo
4.6 Importance Sampling
4.6.1 Principles and First Examples
4.6.2 Path-Dependent Options
4.7 Concluding Remarks
5 Quasi-Monte Carlo
6 Discretization Methods
7 Estimating Sensitivities
8 Pricing American Options
9 Applications in Risk Management
A Appendix: Convergence and Confidence Intervals
B Appendix: Results from Stochastic Calculus
C Appendix: The Term Structure of Interest Rates
References
Index
