英文标题：
《A cluster driven log-volatility factor model: a deepening on the source
of the volatility clustering》
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作者：
Anshul Verma, Riccardo Junior Buonocore, Tiziana di Matteo
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最新提交年份：
2018
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英文摘要：
We introduce a new factor model for log volatilities that performs dimensionality reduction and considers contributions globally through the market, and locally through cluster structure and their interactions. We do not assume a-priori the number of clusters in the data, instead using the Directed Bubble Hierarchical Tree (DBHT) algorithm to fix the number of factors. We use the factor model and a new integrated non parametric proxy to study how volatilities contribute to volatility clustering. Globally, only the market contributes to the volatility clustering. Locally for some clusters, the cluster itself contributes statistically to volatility clustering. This is significantly advantageous over other factor models, since the factors can be chosen statistically, whilst also keeping economically relevant factors. Finally, we show that the log volatility factor model explains a similar amount of memory to a Principal Components Analysis (PCA) factor model and an exploratory factor model.
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中文摘要：
我们引入了一个新的对数波动率因子模型，该模型进行了维度缩减，并考虑了全球通过市尝本地通过集群结构及其相互作用的贡献。我们不预先假定数据中的聚类数，而是使用有向气泡层次树（DBHT）算法来固定因子数。我们使用因子模型和一种新的综合非参数代理来研究波动性如何影响波动性聚类。在全球范围内，只有市场参与了波动性集群。就某些集群而言，集群本身在统计上对波动性集群有贡献。这比其他因素模型有显著优势，因为可以从统计上选择因素，同时也保持经济相关因素。最后，我们表明，对数波动率因子模型解释了与主成分分析（PCA）因子模型和探索性因子模型相似的记忆量。
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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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