英文标题：
《Dynamic Dependence Modeling in financial time series》
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作者：
Yali Dou, Haiyan Liu, Georgios Aivaliotis
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最新提交年份：
2019
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英文摘要：
  This paper explores the dependence modeling of financial assets in a dynamic way and its critical role in measuring risk. Two new methods, called Accelerated Moving Window method and Bottom-up method are proposed to detect the change of copula. The performance of these two methods together with Binary Segmentation \\cite{vostrikova1981detection} and Moving Window method \\cite{guegan2009forecasting} is compared based on simulated data. The best-performing method is applied to Standard \\& Poor 500 and Nasdaq indices. Value-at-Risk and Expected Shortfall are computed from the dynamic and the static model respectively to illustrate the effectiveness of the best method as well as the importance of dynamic dependence modeling through backtesting.
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中文摘要：
本文探讨了金融资产的动态依赖模型及其在风险度量中的关键作用。提出了两种新的copula变化检测方法：加速移动窗口法和自底向上法。在模拟数据的基础上，比较了这两种方法与二值分割方法{vostrikova1981detection}和移动窗口方法{guegan2009forecasting}的性能。表现最好的方法适用于标准普尔500指数和纳斯达克指数。分别从动态模型和静态模型计算风险价值和预期短缺，以说明最佳方法的有效性以及通过回溯测试进行动态依赖建模的重要性。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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