摘要翻译：
本文考虑了不同股票市场指数、汇率和利率集合的每日金融数据，通过估计马尔可夫切换多重分形模型(MSM)的一个简单规范，分析了它们的多尺度性质。为了了解估计的模型如何很好地捕捉数据的时间依赖性，我们估计并比较了估计的MSM模型的经验数据和模拟数据的标度指数$h(q)$(q=1,2$)。在大多数情况下，多重分形模型似乎产生了与经验标度律一致的“明显”长记忆。
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英文标题：
《True and Apparent Scaling: The Proximity of the Markov-Switching
  Multifractal Model to Long-Range Dependence》
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作者：
Ruipeng Liu, T. Di Matteo, Thomas Lux
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最新提交年份：
2007
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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：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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英文摘要：
  In this paper, we consider daily financial data of a collection of different stock market indices, exchange rates, and interest rates, and we analyze their multi-scaling properties by estimating a simple specification of the Markov-switching multifractal model (MSM). In order to see how well the estimated models capture the temporal dependence of the data, we estimate and compare the scaling exponents $H(q)$ (for $q = 1, 2$) for both empirical data and simulated data of the estimated MSM models. In most cases the multifractal model appears to generate `apparent' long memory in agreement with the empirical scaling laws.
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PDF链接：
https://arxiv.org/pdf/0704.1338