Dynamic correlation network analysis of financial asset returns with network clustering
Abstract 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...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2017-05-01
|
| Series: | Applied Network Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/s41109-017-0031-6 |
| _version_ | 1828237291326275584 |
|---|---|
| author | Takashi Isogai |
| author_facet | Takashi Isogai |
| author_sort | Takashi Isogai |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-12T20:45:27Z |
| format | Article |
| id | doaj.art-e970867e47da499bb4fa761e9e6772ed |
| institution | Directory Open Access Journal |
| issn | 2364-8228 |
| language | English |
| last_indexed | 2024-04-12T20:45:27Z |
| publishDate | 2017-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Network Science |
| spelling | doaj.art-e970867e47da499bb4fa761e9e6772ed2022-12-22T03:17:17ZengSpringerOpenApplied Network Science2364-82282017-05-012113010.1007/s41109-017-0031-6Dynamic correlation network analysis of financial asset returns with network clusteringTakashi Isogai0Bank of JapanAbstract 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.http://link.springer.com/article/10.1007/s41109-017-0031-6Financial asset returnsCorrelation networkDynamic correlationNetwork clusteringDimensionality reduction |
| spellingShingle | Takashi Isogai Dynamic correlation network analysis of financial asset returns with network clustering Applied Network Science Financial asset returns Correlation network Dynamic correlation Network clustering Dimensionality reduction |
| title | Dynamic correlation network analysis of financial asset returns with network clustering |
| title_full | Dynamic correlation network analysis of financial asset returns with network clustering |
| title_fullStr | Dynamic correlation network analysis of financial asset returns with network clustering |
| title_full_unstemmed | Dynamic correlation network analysis of financial asset returns with network clustering |
| title_short | Dynamic correlation network analysis of financial asset returns with network clustering |
| title_sort | dynamic correlation network analysis of financial asset returns with network clustering |
| topic | Financial asset returns Correlation network Dynamic correlation Network clustering Dimensionality reduction |
| url | http://link.springer.com/article/10.1007/s41109-017-0031-6 |
| work_keys_str_mv | AT takashiisogai dynamiccorrelationnetworkanalysisoffinancialassetreturnswithnetworkclustering |