Isogai Takashi
Bank of Japan, 2-1-1 Nihonbashi-Hongokucho, Chuo-ku, Tokyo, 103-0021 Japan.
2Tokyo Metropolitan University, 1-4-1 Marunouchi, Chioda-ku, Tokyo, 100-0005 Japan.
Appl Netw Sci. 2017;2(1):8. doi: 10.1007/s41109-017-0031-6. Epub 2017 May 23.
In this study, we propose a novel approach to analyze a dynamic correlation network of highly volatile financial asset returns by using a network clustering algorithm to deal with high dimensionality issues. We analyze the dynamic correlation network of selected Japanese stock returns as an empirical study of the correlation dynamics at the market level by applying the proposed method. Two types of network clustering algorithms are employed for the dimensionality reduction. Firstly, several stock groups instead of the existing business sector classification are generated by the hierarchical recursive network clustering of filtered stock returns in order to overcome the high dimensionality problem due to the large number of stocks. The stock returns are then filtered in advance to control for volatility fluctuations that can distort the correlation between stocks. Thus, the correlation network of individual stock returns is transformed into a correlation network of group-based portfolio returns. Secondly, the reduced size of the correlation network is extended to a dynamic one by using a model-based correlation estimation method. A time series of adjacency matrices is created on a daily basis as a dynamic correlation network from the estimation results. Then, the correlation network is summarized into only three representative correlation networks by clustering along the time axis. Some intertemporal comparisons of the dynamic correlation network are conducted by examining the differences between the three sub-period networks. Our dynamic correlation network analysis framework is not limited to stock returns, but can be applied to many other financial and non-financial volatile time series data.
在本研究中，我们提出了一种新颖的方法，通过使用网络聚类算法来处理高维问题，以分析高波动性金融资产回报的动态相关网络。我们应用所提出的方法，对选定的日本股票回报的动态相关网络进行分析，作为市场层面相关动态的实证研究。采用了两种类型的网络聚类算法进行降维。首先，通过对过滤后的股票回报进行层次递归网络聚类，生成几个股票组，而不是现有的商业部门分类，以克服由于股票数量众多而导致的高维问题。然后预先对股票回报进行过滤，以控制可能扭曲股票之间相关性的波动率波动。这样，单个股票回报的相关网络就被转化为基于组的投资组合回报的相关网络。其次，通过使用基于模型的相关估计方法，将相关网络的缩小规模扩展为动态网络。根据估计结果，每天创建一个邻接矩阵的时间序列作为动态相关网络。然后，通过沿时间轴聚类，将相关网络总结为仅三个具有代表性的相关网络。通过检查三个子时期网络之间的差异，对动态相关网络进行了一些跨期比较。我们的动态相关网络分析框架不仅限于股票回报，还可以应用于许多其他金融和非金融波动性时间序列数据。
