Building a dynamic correlation network for fat-tailed financial asset returns
Building a dynamic correlation network for fat-tailed financial asset returns
复制标题
构建厚尾金融资产收益的动态关联网络
DOI：
10.1007/s41109-016-0008-x
复制
发表时间：
2016
影响因子：
2.2
通讯作者：
Takashi Isogai
中科院分区：
文献类型：
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作者：
Takashi Isogai
本文提出了一种新的方法来建立一个动态相关网络的高度波动的金融资产收益。该方法通过滤波方法避免了估计金融资产收益率动态相关矩阵时的虚假相关问题。一个多元波动模型，DCC-GARCH，过滤厚尾收益。该方法被证明是更可靠的检测动态变化的相关矩阵相比，广泛使用的方法计算时间相关矩阵在一个固定大小的移动窗口，这可能有根本性的问题时，适用于厚尾收益。我们将该方法应用于日本股票收益率，以观察动态网络的变化作为案例研究。然后将估计的时间相关矩阵与使用传统方法计算的时间相关矩阵进行比较，以突出所提出方法的优点。两种类型的指标，即最大特征值和余弦距离的措施，被引入到识别显着的应力事件的初始筛选的相关矩阵的显着变化。一个更详细的网络为基础的分析，然后进行检查从网络邻接矩阵计算的拓扑措施。压力期间的相关网络的密度较高，异质性较低，清楚地观察到，而股票收益率的相关网络被证明是强大的时间。本文所讨论的方法不仅限于股票收益率，它也可以应用于其他金融和非金融时间序列的高波动性的动态相关网络的构建。
In this paper, a novel approach to building a dynamic correlation network of highly volatile financial asset returns is presented. Our method avoids the spurious correlation problem when estimating the dynamic correlation matrix of financial asset returns by using a filtering approach. A multivariate volatility model, DCC–GARCH, is employed to filter the fat-tailed returns. The method is proven to be more reliable for detecting dynamic changes in the correlation matrix compared with the widely used method of calculating time-dependent correlation matrices over a fixed size moving window, which can have fundamental problems when applied to fat-tailed returns. We apply the method to selected Japanese stock returns to observe the dynamic network changes as a case study. The estimated time-dependent correlation matrices are then compared with those calculated by using the traditional method to highlight the advantages of the proposed method. Two types of indicators, namely the largest eigenvalue and cosine distance measures, are introduced to identify significant changes in the correlation matrix for an initial screening of remarkable stress events. A more detailed network-based analysis is then conducted by examining topological measures calculated from the network adjacency matrices. The higher density and lower heterogeneity of the correlation network during stress periods are clearly observed, while the correlation network of stock returns is shown to be robust with regard to time. The method discussed in this paper is not limited to stock returns; it can also be applied to build a dynamic correlation network of other financial and non-financial time series with high volatility.
影响因子：
2.1
作者：
Isogai, Takashi
通讯作者：
Isogai, Takashi
影响因子：
1.6
作者：
Mantegna, RN
通讯作者：
Mantegna, RN
DOI：
10.1109/sitis.2015.39
发表时间：
2015
期刊：
2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)
影响因子：
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作者：
Takashi Isogai
通讯作者：
Takashi Isogai