数理金融初步（英文版第3版）pdf/txt下载_在线阅读全文

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　　本书内容选择得当、结构安排合理，既适合作为高等院校学生（包括财经类专业及应用数学专业）的教材，同时也适合从事金融工作的人员阅读。

内容简介

　　罗斯所著的《数理金融初步(英文版第3版)》清晰简洁地阐述了数理金融学的基本问题，主要包括套利、Black-Scholes期权定价公式以及效用函数、*资产组合原理、资本资产定价模型等知识，并将书中所讨论的问题的经济背景、解决这些问题的数学方法和基本思想系统地展示给读者。

目　　录

Introduction and Preface
1 Probability
1.1 Probabilities and Events
1.2 Conditional Probability
1.3 Random Variables and Expected Values
1.4 Covariance and Correlation
1.5 Conditional Expectation
1.6 Exercises
2 Normal Random Variables
2.1 Continuous Random Variables
2.2 Normal Random Variables
2.3 Properties of Normal Random Variables
2.4 The Central Limit Theorem
2.5 Exercises

Introduction and Preface 
1 Probability 
1.1 Probabilities and Events 
1.2 Conditional Probability 
1.3 Random Variables and Expected Values 
1.4 Covariance and Correlation 
1.5 Conditional Expectation 
1.6 Exercises 
2 Normal Random Variables 
2.1 Continuous Random Variables 
2.2 Normal Random Variables 
2.3 Properties of Normal Random Variables 
2.4 The Central Limit Theorem 
2.5 Exercises 
3 Brownian Motion and Geometric Brownian Motion 
3.1 Brownian Motion 
3.2 Brownian Motion as a Limit of Simpler Models 
3.3 Geometric Brownian Motion 
3.3.1 Geometric Brownian Motion as a Limit of Simpler Models 
3.4 *The Maximum Variable 
3.5 The Cameron-Martin Theorem 
3.6 Exercises 
4 Interest Rates and Present Value Analysis 
4.1 Interest Rates 
4.2 Present Value Analysis 
4.3 Rate of Return 
4.4 Continuously Varying Interest Rates 
4.5 Exercises 
5 Pricing Contracts via Arbitrage 
5.1 An Example in Options Pricing 
5.2 Other Examples of Pricing via Arbitrage 
5.3 Exercises 
6 The Arbitrage Theorem 
6.1 The Arbitrage Theorem 
6.2 The Multiperiod Binomial Model 
6.3 Proof of the Arbitrage Theorem 
6.4 Exercises 
7 The Black-Scboles Formula 
7.1 Introduction 
7.2 The Black-Scholes Formula 
7.3 Properties of the Black-Scholes Option Cost 
7.4 The Delta Hedging Arbitrage Strategy 
7.5 Some Derivations 
7.5.1 The Black-Scholes Formula 
7.5.2 The Partial Derivatives 
7.6 European Put Options 
7.7 Exercises 
8 Additional Results on Options 
8.1 Introduction 
8.2 Call Options on Dividend-Paying Securities 
8.2.1 The Dividend for Each Share of the Security 
Is Paid Continuously in Time at a Rate Equal 
to a Fixed Fraction f of the Price of the 
Security 
8.2.2 For Each Share Owned, a Single Payment of 
fS(td) IS Made at Time td 
8.2.3 For Each Share Owned, a Fixed Amount D Is 
to Be Paid at Time td 
8.3 Pricing American Put Options 
8.4 Adding Jumps to Geometric Brownian Motion 
8.4.1 When the Jump Distribution Is Lognormal 
8.4.2 When the Jump Distribution Is General 
8.5 Estimating the Volatility Parameter 
8.5.1 Estimating a Population Mean and Variance 
8.5.2 The Standard Estimator of Volatility 
8.5.3 Using Opening and Closing Data 
8.5.4 Using Opening, Closing, and High-Low Data 
8.6 Some Comments 
8.6.1 When the Option Cost Differs from the Black-Scholes
Formula 
8.6.2 When the Interest Rate Changes 
8.6.3 Final Comments 
8.7 Appendix 
8.8 Exercises 
9 Valuing by Expected Utility 
9.1 Limitations of Arbitrage Pricing 
9.2 Valuing Investments by Expected Utility 
9.3 The Portfolio Selection Problem 
9.3.1 Estimating Covariances 
9.4 Value at Risk and Conditional Value at Risk 
9.5 The Capital Assets Pricing Model 
9.6 Rates of Return: Single-Period and Geometric 
Brownian Motion 
9.7 Exercises 
10 Stochastic Order Relations 
10.1 First-Order Stochastic Dominance 
10.2 Using Coupling to Show Stochastic Dominance 
10.3 Likelihood Ratio Ordering 
10.4 A Single-Period Investment Problem 
10.5 Second-Order Dominance 
10.5.1 Normal Random Variables 
10.5.2 More on Second-Order Dominance 
10.6 Exercises 
11 Optimization Models 
11.1 Introduction 
11.2 A Deterministic Optimization Model 
11.2.1 A General Solution Technique Based on 
Dynamic Programming 
11.2.2 A Solution Technique for Concave 
Return Functions 
11.2.3 The Knapsack Problem 
11.3 Probabilistic Optimization Problems 
11.3.1 A Gambling Model with Unknown Win Probabilities 
11.3.2 An Investment Allocation Model 
11.4 Exercises 
12 Stochastic Dynamic Programming 
12.1 The Stochastic Dynamic Programming Problem 
12.2 Infinite Time Models 
12.3 Optimal Stopping Problems 
12.4 Exercises 
13 Exotic Options 
13.1 Introduction 
13.2 Barrier Options 
13.3 Asian and Lookback Options 
13.4 Monte Carlo Simulation 
13.5 Pricing Exotic Options by Simulation 
13.6 More Efficient Simulation Estimators 
13.6.1 Control and Antithetic Variables in the 
Simulation of Asian and Lookback 
Option Valuations 
13.6.2 Combining Conditional Expectation and 
Importance Sampling in the Simulation of 
Barrier Option Valuations 
13.7 Options with Nonlinear Payoffs 
13.8 Pricing Approximations via Multiperiod Binomial Models 
13.9 Continuous Time Approximations of Barrier and Lookback
Options 
13.10 Exercises 
14 Beyond Geometric Brownian Motion Models 
14.1 Introduction 
14.2 Crude Oil Data 
14.3 Models for the Crude Oil Data 
14.4 Final Comments 
15 Autoregressive Models and Mean Reversion 
15.1 The Autoregressive Model 
15.2 Valuing Options by Their Expected Return 
15.3 Mean Reversion 
15.4 Exercises 
Index

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