摘要翻译：
金融工具的波动性很少是恒定的，通常随时间而变化。这就产生了一种称为波动性群集的现象，即价格在一天的大幅波动之后，连续几天的类似大幅波动，形成了时间群集。GARCH模型将波动率视为一个漂移过程，通常用于捕捉这种行为。然而，研究表明，波动性通常更好地用结构突变模型来描述，在这种模型中，波动性除了漂移之外还经历了突然的跳跃。将这些跳跃整合到GARCH方法中的大多数努力都导致了模型，这些模型要么非常需要计算，要么对仪器的分布做出了有问题的假设，通常假设它们是高斯的。我们提出了一种新的方法，它利用非参数统计的思想来识别结构断点，而不做这样的分布假设，然后模型在每个识别的区域内分别漂移。使用我们的方法，我们研究了几个主要股票指数的波动性，并发现我们的方法可以给出一个潜在的改进拟合比更常用的技术。
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英文标题：
《Modeling Financial Volatility in the Presence of Abrupt Changes》
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作者：
Gordon J. Ross
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最新提交年份：
2012
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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：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  The volatility of financial instruments is rarely constant, and usually varies over time. This creates a phenomenon called volatility clustering, where large price movements on one day are followed by similarly large movements on successive days, creating temporal clusters. The GARCH model, which treats volatility as a drift process, is commonly used to capture this behavior. However research suggests that volatility is often better described by a structural break model, where the volatility undergoes abrupt jumps in addition to drift. Most efforts to integrate these jumps into the GARCH methodology have resulted in models which are either very computationally demanding, or which make problematic assumptions about the distribution of the instruments, often assuming that they are Gaussian. We present a new approach which uses ideas from nonparametric statistics to identify structural break points without making such distributional assumptions, and then models drift separately within each identified regime. Using our method, we investigate the volatility of several major stock indexes, and find that our approach can potentially give an improved fit compared to more commonly used techniques.
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PDF链接：
https://arxiv.org/pdf/1212.6016