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Stochastic Calculus for Finance evolved from the first ten years of the Carnegie Mellon Professional Master's program in Computational Finance. The content of this book has been used successfully with students whose mathematics background consists of calculus and calculus-based probability. The text gives both precise statements of results, plausibility arguments, and even some proofs, but more importantly intuitive explanations developed and refine through classroom experience with this material are provided. The book includes a self-contained treatment of the probability theory needed for stochastic calculus, including Brownian motion and its properties. Advanced topics include foreign exchange models, forward measures, and jump-diffusion processes. This book is being published in two volumes. This second volume develops stochastic calculus, martingales, risk-neutral pricing, exotic options and term structure models, all in continuous time. Master's level students and researchers in mathematical finance and financial engineering will find this book useful.

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在线阅读地址：Stochastic Calculus for Finance II在线阅读

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一般地，不论最初财富x_0以及delta_n如何选择，我们用符号delta_n来表示资产组合中股票的数量，用x_n来表示相应的资产组合的价值。如果所选择的x_0和delta_复制了一个衍生证券，我们用符号v_n来代替x_n，并称之为时刻n的衍生证券（无套利）价格。

一个随机变了是一个将样本空间Ω映射到实数集的函数。一个随机变量的分布是对随机变量取不同值的概率的具体描述。随机变量不是分布，分布也不是随机变量。在通过历史数据估计得到的真实概率测度与风险中性概率测度之间转换时，这一点非常重要。测度的变换将改变随机变量的分布，但是不会改变随机变量本身。

股价涨跌变化的概率并不相干，关键是涨跌变动的幅度（即u和d的值），在二叉树模型中，衍生证券的价格取决于可能的股票价格路径的集合，而非这些路径的可能性。

这一无套利条件唯一决定了衍生证券在所有时刻的价格。

……

在任何时刻，股价为下一时刻两种可能价格的风险中性均值的贴现。换言之，在风险中性概率下，股票的平均回报率为r，与货币市场的回报率相同。因此，如果这些概率果真可以决定抛掷硬币的结果（事实上不可能），那么投资者无论从事股票市场交易还是从事货币市场交易，都将获得相同的平均回报率。

……

我们希望，无论抛掷硬币的结果如何，用以对冲的资产组合价格总能与衍生证券的支付相一致。换言之，这种对冲必须对所有股票价格路径有效。引入风险中性概率使我们可以给出上述论证并且求的方程组的解。引入任何其他概率都不会有与此相同的论证，因为只有在风险中性概率下，无论如何投资，资产组合的平均回报率均为r。风险中性概率提供了求解这一系列方程的捷径，而真实概率无助于方程的求解。

独立性：如果x只依赖从第n+1次至第N次抛掷硬币的结果，那么：En(x)=E(x)

在风险中性测度下，贴现股票价格过程是一个鞅，即在每一个时刻n对任意的抛掷硬币结果序列成立。

一般地，如果一个模型中存在一个风险中性测度（就哪条价格路径有零概率而言，该测度与真实概率测度一致；同时在该测度下，所有基础资产的贴现价格过程为鞅），那么这个模型中就不存在套利。这一结论又是被称为资产定价第一基本定理。

书籍介绍

在线阅读本书

Stochastic Calculus for Finance evolved from the first ten years of the Carnegie Mellon Professional Master's program in Computational Finance. The content of this book has been used successfully with students whose mathematics background consists of calculus and calculus-based probability. The text gives both precise statements of results, plausibility arguments, and even some proofs, but more importantly intuitive explanations developed and refine through classroom experience with this material are provided. The book includes a self-contained treatment of the probability theory needed for stochastic calculus, including Brownian motion and its properties. Advanced topics include foreign exchange models, forward measures, and jump-diffusion processes. This book is being published in two volumes. This second volume develops stochastic calculus, martingales, risk-neutral pricing, exotic options and term structure models, all in continuous time. Master's level students and researchers in mathematical finance and financial engineering will find this book useful.