高维金融时间序列相关性建模

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英文标题：
《Modelling of dependence in high-dimensional financial time series by
  cluster-derived canonical vines》
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作者：
David Walsh-Jones, Daniel Jones, Christoph Reisinger
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最新提交年份：
2014
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英文摘要：
  We extend existing models in the financial literature by introducing a cluster-derived canonical vine (CDCV) copula model for capturing high dimensional dependence between financial time series. This model utilises a simplified market-sector vine copula framework similar to those introduced by Heinen and Valdesogo (2008) and Brechmann and Czado (2013), which can be applied by conditioning asset time series on a market-sector hierarchy of indexes. While this has been shown by the aforementioned authors to control the excessive parameterisation of vine copulas in high dimensions, their models have relied on the provision of externally sourced market and sector indexes, limiting their wider applicability due to the imposition of restrictions on the number and composition of such sectors. By implementing the CDCV model, we demonstrate that such reliance on external indexes is redundant as we can achieve equivalent or improved performance by deriving a hierarchy of indexes directly from a clustering of the asset time series, thus abstracting the modelling process from the underlying data.
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中文摘要：
我们通过引入集群衍生的规范藤（CDCV）copula模型来捕获金融时间序列之间的高维相关性，从而扩展了金融文献中的现有模型。该模型使用了简化的市场部门vine copula框架，类似于Heinen和Valdesogo（2008）以及Brechmann和Czado（2013）提出的框架，可以通过调整市场部门指数层次结构上的资产时间序列来应用该框架。虽然上述作者已经证明，这可以控制高维藤蔓连接函数的过度参数化，但他们的模型依赖于外部来源的市场和部门指数，由于对此类部门的数量和组成施加限制，限制了其更广泛的适用性。通过实施CDCV模型，我们证明了这种对外部指数的依赖是多余的，因为我们可以通过直接从资产时间序列的聚类中导出指数层次结构，从而从基础数据中抽象建模过程，从而实现同等或改进的性能。
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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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关键词：金融时间序列 序列相关性 序列相关 时间序列 相关性