摘要翻译：
通过考虑未来某一特定时刻资产价值的条件概率密度过程的动力学，我们建立了资产价格和相关衍生品的动力学模型。在价格过程由布朗运动驱动的情况下，导出了条件概率密度动力学的一个相关的“主方程”，并以积分形式表示。对于条件密度过程的“模型”，我们指的是主方程的解，以及(a)初始密度和(b)密度的波动性结构的说明。假设波动性结构在任何时候，对于每一个密度参数的值，都是到该时刻为止的密度历史的函数。在实践中，一个指定函数模，充分的参数自由度，以允许输入额外的选项数据，除了隐含在初始密度。该方案十分灵活，可根据期权市场的性质和所处理的估值问题的类别输入各种类型的数据。详细研究了各种实例，并在某些情况下给出了精确解。
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英文标题：
《Conditional Density Models for Asset Pricing》
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作者：
Damir Filipovi\'c, Lane P. Hughston, Andrea Macrina
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最新提交年份：
2011
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Pricing of Securities        证券定价
分类描述：Valuation and hedging of financial securities, their derivatives, and structured products
金融证券及其衍生产品和结构化产品的估值和套期保值
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一级分类：Mathematics        数学
二级分类：Probability        概率
分类描述：Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory
概率论与随机过程的理论与应用：例如中心极限定理，大偏差，随机微分方程，统计力学模型，排队论
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一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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英文摘要：
  We model the dynamics of asset prices and associated derivatives by consideration of the dynamics of the conditional probability density process for the value of an asset at some specified time in the future. In the case where the price process is driven by Brownian motion, an associated "master equation" for the dynamics of the conditional probability density is derived and expressed in integral form. By a "model" for the conditional density process we mean a solution to the master equation along with the specification of (a) the initial density, and (b) the volatility structure of the density. The volatility structure is assumed at any time and for each value of the argument of the density to be a functional of the history of the density up to that time. In practice one specifies the functional modulo sufficient parametric freedom to allow for the input of additional option data apart from that implicit in the initial density. The scheme is sufficiently flexible to allow for the input of various types of data depending on the nature of the options market and the class of valuation problem being undertaken. Various examples are studied in detail, with exact solutions provided in some cases.
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PDF链接：
https://arxiv.org/pdf/1010.4384