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Bayesian estimation of the Hurst parameter of fractional Brownian motion

机译:分数布朗运动的赫斯特参数的贝叶斯估计

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摘要

The primary purpose of this study was to find Bayesian estimates for the Hurst dimension of a Fractional Brownian motion with a Beta prior when the process is observed at discrete times. Overestimation is observed though the overestimation is less severe as real H goes up. In addition, the estimated H decreases as Beta parameters go up given an Alpha value. In contrast, the estimated H increases as Alpha parameters go up given a Beta value. For the real-world data, the 2011 daily Taiwan stock index was used and the estimated Hurst index was 0.21.
机译:这项研究的主要目的是找到在离散时间观察到贝叶斯分数的布朗运动的Hurst维数的贝叶斯估计值。尽管实际H升高,高估的程度较小,但仍观察到高估。此外,在给定Alpha值的情况下,随着Beta参数的增加,估计的H值会降低。相反,在给定Beta值的情况下,估计的H随着Alpha参数的增加而增加。对于真实世界的数据,使用2011年台湾日股指数,估计的赫斯特指数为0.21。

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