英文标题：
《Dynamic correlations at different time-scales with Empirical Mode
Decomposition》
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作者：
Noemi Nava and T. Di Matteo and Tomaso Aste
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最新提交年份：
2017
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英文摘要：
The Empirical Mode Decomposition (EMD) provides a tool to characterize time series in terms of its implicit components oscillating at different time-scales. We apply this decomposition to intraday time series of the following three financial indices: the S\\&P 500 (USA), the IPC (Mexico) and the VIX (volatility index USA), obtaining time-varying multidimensional cross-correlations at different time-scales. The correlations computed over a rolling window are compared across the three indices, across the components at different time-scales, at different lags and over time. We uncover a rich heterogeneity of interactions which depends on the time-scale and has important led-lag relations which can have practical use for portfolio management, risk estimation and investments.
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中文摘要：
经验模态分解（EMD）提供了一种工具，用其隐式分量在不同时间尺度上振荡来表征时间序列。我们将此分解应用于以下三个金融指数的日内时间序列：标准普尔500指数（美国）、IPC指数（墨西哥）和VIX指数（美国波动率指数），获得了不同时间尺度下的时变多维互相关。在滚动窗口中计算的相关性在三个指数、不同时间尺度、不同滞后和随时间变化的分量之间进行比较。我们发现了依赖于时间尺度的交互作用的丰富异质性，并且具有重要的led滞后关系，可用于投资组合管理、风险估计和投资。
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分类信息：

一级分类：Computer Science 计算机科学
二级分类：Computational Engineering, Finance, and Science 计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
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一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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