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IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)最新文献

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Evaluation of common models used in the estimation of the historical volatility applied to trade-by-trade stock returns data from the U.S. and Mexico 对美国和墨西哥的逐笔交易股票收益数据中用于估计历史波动率的常用模型进行评估
J. W. Dalle Molle
The objective of the investigation is to characterize the fundamental statistical properties of various measures that have been used to model the volatility of the trade-by-trade returns process of common stock. Ideally we would like the observations of the returns process or the logarithm of the return process to have been generated as independent and identically distributed Gaussian variates such that its mean is a constant and the variance is a constant or a linear function of time. This is because the stationary Gaussian distribution is the only probability distribution that is completely characterized by two parameters (mean and variance). When the logarithm of the percentage change in price is used to measure volatility it lends itself to the use of the geometric Brownian motion (which is based on the logarithm of the percentage changes having a Gaussian distribution).
调查的目的是描述各种测量的基本统计特性,这些测量被用来模拟普通股逐交易回报过程的波动性。理想情况下,我们希望返回过程的观测值或返回过程的对数被生成为独立和同分布的高斯变量,这样它的平均值是一个常数,方差是一个常数或时间的线性函数。这是因为平稳高斯分布是唯一完全由两个参数(均值和方差)表征的概率分布。当价格百分比变化的对数用于测量波动性时,它适合使用几何布朗运动(它基于具有高斯分布的百分比变化的对数)。
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IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)
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