英文标题：
《Techniques for multifractal spectrum estimation in financial time series》
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作者：
Petr Jizba and Jan Korbel
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最新提交年份：
2016
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英文摘要：
Multifractal analysis is one of the important approaches that enables us to measure the complexity of various data via the scaling properties. We compare the most common techniques used for multifractal exponents estimation from both theoretical and practical point of view. Particularly, we discuss the methods based on estimation of R\\\'enyi entropy, which provide a powerful tool especially in presence of heavy-tailed data. To put some flesh on bare bones, all methods are compared on various real financial datasets, including daily and high-frequency data.
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中文摘要：
多重分形分析是一种重要的方法，它使我们能够通过尺度特性来衡量各种数据的复杂性。我们从理论和实践的角度比较了最常用的多重分形指数估计技术。特别是，我们讨论了基于R趵enyi熵估计的方法，这为重尾数据的存在提供了强有力的工具。为了让大家了解一些真实情况，所有方法都在各种真实的金融数据集上进行了比较，包括日常数据和高频数据。
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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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