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TECHNIQUES FOR MULTIFRACTAL SPECTRUM ESTIMATION IN FINANCIAL TIME SERIES

机译:金融时间序列中的多分形谱估计技术

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摘要

We show that a multifractal analysis offers a new and potentially promising avenue for quantifying the complexity of various time series. In particular, we compare the most common techniques used for multifractal scaling exponents estimation. This is done from both a theoretical and phenomenological point of view. In our discussion we specifically focus on methods based on estimation of Renyi entropy, which provide a powerful tool especially in the presence of heavy-tailed data. As a testbed for the applicability of above multifractal methods we use various real financial datasets, including both daily and high-frequency data.
机译:我们表明,多重分形分析为量化各种时间序列的复杂性提供了一种新的且可能很有希望的途径。特别是,我们比较了用于多重分形标度指数估计的最常用技术。从理论和现象学的角度来看都是这样做的。在我们的讨论中,我们特别关注基于Renyi熵估计的方法,这些方法提供了强大的工具,尤其是在存在重尾数据的情况下。作为上述多重分形方法的适用性的试验平台,我们使用了各种实际的财务数据集,包括每日和高频数据。

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