Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach
Financial internationalization leads to similar fluctuations and spillover effects in financial markets around the world, resulting in cross-border financial risks. This study examines comovements across G20 international stock markets while considering the volatility similarity and spillover effect...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8872307 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832567771530002432 |
|---|---|
| author | Shanglei Chai Zhen Zhang Mo Du Lei Jiang |
| author_facet | Shanglei Chai Zhen Zhang Mo Du Lei Jiang |
| author_sort | Shanglei Chai |
| collection | DOAJ |
| description | Financial internationalization leads to similar fluctuations and spillover effects in financial markets around the world, resulting in cross-border financial risks. This study examines comovements across G20 international stock markets while considering the volatility similarity and spillover effects. We provide a new approach using an ICA- (independent component analysis-) based ARMA-APARCH-M model to shed light on whether there are spillover effects among G20 stock markets with similar dynamics. Specifically, we first identify which G20 stock markets have similar volatility features using a fuzzy C-means time series clustering method and then investigate the dominant source of volatility spillovers using the ICA-based ARMA-APARCH-M model. The evidence has shown that the ICA method can more accurately capture market comovements with nonnormal distributions of the financial time series data by transforming the multivariate time series into statistically independent components (ICs). Our findings indicate that the G20 stock markets are clustered into three categories according to volatility similarity. There are spillover effects in stock market comovements of each group and the dominant source can be identified. This study has important implications for investors in international financial markets and for policymakers in G20 countries. |
| format | Article |
| id | doaj-art-03161414aad44613810dea9e5bb373aa |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-03161414aad44613810dea9e5bb373aa2025-02-03T01:00:28ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/88723078872307Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M ApproachShanglei Chai0Zhen Zhang1Mo Du2Lei Jiang3School of Business, Shandong Normal University, Jinan 250014, ChinaInstitute of Systems Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Accounting, Shandong Youth University of Political Science, Jinan 250103, ChinaSchool of Economics, Zhejiang University of Finance & Economics, Hangzhou 310018, ChinaFinancial internationalization leads to similar fluctuations and spillover effects in financial markets around the world, resulting in cross-border financial risks. This study examines comovements across G20 international stock markets while considering the volatility similarity and spillover effects. We provide a new approach using an ICA- (independent component analysis-) based ARMA-APARCH-M model to shed light on whether there are spillover effects among G20 stock markets with similar dynamics. Specifically, we first identify which G20 stock markets have similar volatility features using a fuzzy C-means time series clustering method and then investigate the dominant source of volatility spillovers using the ICA-based ARMA-APARCH-M model. The evidence has shown that the ICA method can more accurately capture market comovements with nonnormal distributions of the financial time series data by transforming the multivariate time series into statistically independent components (ICs). Our findings indicate that the G20 stock markets are clustered into three categories according to volatility similarity. There are spillover effects in stock market comovements of each group and the dominant source can be identified. This study has important implications for investors in international financial markets and for policymakers in G20 countries.http://dx.doi.org/10.1155/2020/8872307 |
| spellingShingle | Shanglei Chai Zhen Zhang Mo Du Lei Jiang Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach Complexity |
| title | Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach |
| title_full | Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach |
| title_fullStr | Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach |
| title_full_unstemmed | Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach |
| title_short | Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach |
| title_sort | volatility similarity and spillover effects in g20 stock market comovements an ica based arma aparch m approach |
| url | http://dx.doi.org/10.1155/2020/8872307 |
| work_keys_str_mv | AT shangleichai volatilitysimilarityandspillovereffectsing20stockmarketcomovementsanicabasedarmaaparchmapproach AT zhenzhang volatilitysimilarityandspillovereffectsing20stockmarketcomovementsanicabasedarmaaparchmapproach AT modu volatilitysimilarityandspillovereffectsing20stockmarketcomovementsanicabasedarmaaparchmapproach AT leijiang volatilitysimilarityandspillovereffectsing20stockmarketcomovementsanicabasedarmaaparchmapproach |