摘要翻译：
长记忆和波动性聚集是金融市场中经常涉及的两个程式化事实。传统上，这些现象的研究是基于条件异方差模型，如ARCH、GARCH、IGARCH和FIGARCH等。这些模型的一个优点是它们能够捕捉非线性动力学。研究波动现象的另一个有趣的方法是使用基于熵概念的度量。本文研究了标准普尔500指数、纳斯达克100指数和斯托克50指数的长记忆性和波动性聚类，以比较美国和欧洲市场。此外，我们还将条件异方差模型的结果与熵测度的结果进行了比较。在后者中，我们考察了Shannon熵、Renyi熵和Tsallis熵。结果证实了以前在时间序列中所考虑的非线性动力学的证据。
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英文标题：
《Long Memory and Volatility Clustering: is the empirical evidence
  consistent across stock markets?》
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作者：
Sonia R. Bentes, Rui Menezes, Diana A. Mendes
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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：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：
  Long memory and volatility clustering are two stylized facts frequently related to financial markets. Traditionally, these phenomena have been studied based on conditionally heteroscedastic models like ARCH, GARCH, IGARCH and FIGARCH, inter alia. One advantage of these models is their ability to capture nonlinear dynamics. Another interesting manner to study the volatility phenomena is by using measures based on the concept of entropy. In this paper we investigate the long memory and volatility clustering for the SP 500, NASDAQ 100 and Stoxx 50 indexes in order to compare the US and European Markets. Additionally, we compare the results from conditionally heteroscedastic models with those from the entropy measures. In the latter, we examine Shannon entropy, Renyi entropy and Tsallis entropy. The results corroborate the previous evidence of nonlinear dynamics in the time series considered.
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PDF链接：
https://arxiv.org/pdf/0709.2178