金融时间序列的动态相关建模
Dynamic Dependence Modeling in financial time series
摘要：
本文探讨了金融资产的动态相关性建模及其在风险度量中的重要作用。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。基于仿真数据比较了这两种方法与二值分割方法\cite{vostrikova1981detection}和移动窗口方法\cite{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。分别计算了动态模型和静态模型的风险价值和预期损失，以说明最佳方法的有效性以及通过回测进行动态相关建模的重要性。
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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