摘要翻译:
提出了一种量化复杂时间序列中聚类行为的新方法,并将其应用于金融市场的高频数据。我们发现,无论使用何种数据集,所有数据都表现出波动性聚类特性,而使用GARCH模型过滤了波动性聚类效应的数据显著降低了波动性聚类。结果表明,我们的方法可以度量金融市场的波动性聚集效应。
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英文标题:
《Measuring Volatility Clustering in Stock Markets》
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作者:
Gabjin Oh, Seunghwan Kim, Cheoljun Eom, Taehyuk Kim
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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: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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英文摘要:
We propose a novel method to quantify the clustering behavior in a complex time series and apply it to a high-frequency data of the financial markets. We find that regardless of used data sets, all data exhibits the volatility clustering properties, whereas those which filtered the volatility clustering effect by using the GARCH model reduce volatility clustering significantly. The result confirms that our method can measure the volatility clustering effect in financial market.
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PDF链接:
https://arxiv.org/pdf/0709.2416


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