摘要翻译：
在早期的研究中，利用每日开盘、收盘、高点和低点的信息来估计股票的波动性已经发展起来；高价格和低价格中的附加信息可以被合并以产生无偏（或近无偏）估计量，其方差大大低于简单的开-闭估计量。本文解决了一个比较困难的任务，即根据每只股票每天的开盘、收盘、高点和低点估计两只股票的相关性。如果我们可以获得两个日志价格的线性组合的高值和低值，那么我们可以通过极化使用单变量结果，但这不是可用的数据。实际问题更具挑战性；我们给出了一个方差减半的无偏估计量。
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英文标题：
《Estimating correlation from high, low, opening and closing prices》
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作者：
L. C. G. Rogers, Fanyin Zhou
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最新提交年份：
2008
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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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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英文摘要：
  In earlier studies, the estimation of the volatility of a stock using information on the daily opening, closing, high and low prices has been developed; the additional information in the high and low prices can be incorporated to produce unbiased (or near-unbiased) estimators with substantially lower variance than the simple open--close estimator. This paper tackles the more difficult task of estimating the correlation of two stocks based on the daily opening, closing, high and low prices of each. If we had access to the high and low values of some linear combination of the two log prices, then we could use the univariate results via polarization, but this is not data that is available. The actual problem is more challenging; we present an unbiased estimator which halves the variance.
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PDF链接：
https://arxiv.org/pdf/0804.0162