英文标题：
《The consentaneous model of the financial markets exhibiting spurious
  nature of long-range memory》
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作者：
Vygintas Gontis, Aleksejus Kononovicius
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最新提交年份：
2018
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英文摘要：
  It is widely accepted that there is strong persistence in the volatility of financial time series. The origin of the observed persistence, or long-range memory, is still an open problem as the observed phenomenon could be a spurious effect. Earlier we have proposed the consentaneous model of the financial markets based on the non-linear stochastic differential equations. The consentaneous model successfully reproduces empirical probability and power spectral densities of volatility. This approach is qualitatively different from models built using fractional Brownian motion. In this contribution we investigate burst and inter-burst duration statistics of volatility in the financial markets employing the consentaneous model. Our analysis provides an evidence that empirical statistical properties of burst and inter-burst duration can be explained by non-linear stochastic differential equations driving the volatility in the financial markets. This serves as an strong argument that long-range memory in finance can have spurious nature.
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中文摘要：
人们普遍认为，金融时间序列的波动性具有很强的持续性。观察到的持久性或长程记忆的起源仍然是一个悬而未决的问题，因为观察到的现象可能是一种虚假效应。早些时候，我们提出了基于非线性随机微分方程的金融市场一致性模型。同意模型成功地再现了波动率的经验概率和功率谱密度。这种方法与使用分数布朗运动建立的模型在性质上不同。在本文中，我们采用同意模型研究了金融市场波动性的突发和突发间持续时间统计。我们的分析提供了一个证据，即爆发和爆发间持续时间的经验统计特性可以用驱动金融市场波动的非线性随机微分方程来解释。这有力地证明了金融中的长期记忆可能具有虚假性。
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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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