Structural break in different stock index markets in China
This paper first presents a two-stage change point estimation approach in the framework of online analysis to detect the Chinese stock market abrupt variations during the period from 4 January 2005 to 10 December 2021. As a check, the pruned exact linear time (PELT) algorithm method is applied to de...
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| Vydáno v: | The North American journal of economics and finance Ročník 65; s. 101882 |
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| Jazyk: | angličtina |
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01.03.2023
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| ISSN: | 1062-9408, 1879-0860 |
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| Abstract | This paper first presents a two-stage change point estimation approach in the framework of online analysis to detect the Chinese stock market abrupt variations during the period from 4 January 2005 to 10 December 2021. As a check, the pruned exact linear time (PELT) algorithm method is applied to detect structural changes in the framework of offline analysis in terms of all data. We select four representative indices in Chinese markets to find some important time-stamp tags. The results show that all indices can detect some common events, while the small-cap and small-mid-cap indices can identify local risks such as China’s market freezing. Besides, we find some events such as the global financial crisis and China’s market freezing can incur the inverse anomaly with higher volatility in lower reward.
•Adopt two-stage change point estimation method to trace the structural change online.•Use pruned exact linear time algorithm to detect multiple change points offline.•Four representative indices in Chinese markets are utilized for empirical analysis.•The small-cap and small-mid-cap indices can identify China’s market freezing.•Some financial events incur higher volatility with lower reward. |
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| AbstractList | This paper first presents a two-stage change point estimation approach in the framework of online analysis to detect the Chinese stock market abrupt variations during the period from 4 January 2005 to 10 December 2021. As a check, the pruned exact linear time (PELT) algorithm method is applied to detect structural changes in the framework of offline analysis in terms of all data. We select four representative indices in Chinese markets to find some important time-stamp tags. The results show that all indices can detect some common events, while the small-cap and small-mid-cap indices can identify local risks such as China’s market freezing. Besides, we find some events such as the global financial crisis and China’s market freezing can incur the inverse anomaly with higher volatility in lower reward.
•Adopt two-stage change point estimation method to trace the structural change online.•Use pruned exact linear time algorithm to detect multiple change points offline.•Four representative indices in Chinese markets are utilized for empirical analysis.•The small-cap and small-mid-cap indices can identify China’s market freezing.•Some financial events incur higher volatility with lower reward. |
| ArticleNumber | 101882 |
| Author | Diao, Xundi Li, Boyan |
| Author_xml | – sequence: 1 givenname: Boyan surname: Li fullname: Li, Boyan email: Boyan.19@intl.zju.edu.cn organization: Zhejiang University-University of Illinois at Urbana-Champaign Institute, Zhejiang University, Zhejiang, China – sequence: 2 givenname: Xundi orcidid: 0000-0002-7691-7208 surname: Diao fullname: Diao, Xundi email: xund@sjtu.edu.cn organization: Antai College of Economics & Management, Shanghai Jiao Tong University, Shanghai, China |
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| Cites_doi | 10.1016/j.spa.2011.11.005 10.1080/01621459.2012.737745 10.1007/s10115-016-0987-z 10.1214/aos/1176343001 10.1080/00401706.2018.1438926 10.1016/j.iref.2014.12.011 10.1080/03610920801919692 10.18637/jss.v058.i03 10.2307/2529204 10.1016/j.jeconom.2004.02.008 10.1016/j.iref.2021.10.019 10.1080/07362994.2014.917359 10.1016/j.najef.2019.101126 10.1080/17442508.2013.802791 10.1016/j.jedc.2004.01.005 10.1162/003465397557132 |
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| SubjectTerms | Change point Pruned exact linear time algorithm Significant events Two-stage estimation method |
| Title | Structural break in different stock index markets in China |
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