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《数理金融初步(英文版.第3版)》基于期权定价全面介绍数理金融学的基本问题，数理推导严密，内容深入浅出，适合受过有限数学训练的专业交易员和高等院校相关专业本科生阅读。本书清晰简洁地阐述了套利、black-scholes期权定价公式、效用函数、最优投资组合选择、资本资产定价模型等知识。

第3版在第2版的基础上新增了布朗运动与几何布朗运动、随机序关系、随机动态规划等内容，并且扩展了每一章的习题和参考文献。

Sheldon M．Ross美国南加州大学工业与系统工程系epstein讲座教授。他于1968年在斯坦福大学获得统计学博士学位，1976~2004年在加州大学伯克利分校任教。他发表了大量有关概率与统计方面的学术论文，并出版了多部教材。他还创办了《probability in engineering and informational sciences》杂志并一直担任主编。他是数理统计学会会员，荣获过美国科学家humboldt奖。

《数理金融初步(英文版.第3版)》
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 vax)ring 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-scholes 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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