Data-driven modeling of global financial linkages : Structural break analysis using computational intelligence
Himanshu Goelhimanshugoel571@gmail.comDepartment of ManagementJagan Institute of Management StudiesRohini, Delhi, 110085, India0000-0002-9523-0719View full profile → , *Monika AgarwalCorresponding authormonika.agarwal@jimsindia.orgDepartment of ManagementJagan Institute of Management StudiesRohini, Delhi, 110085, India0000-0002-0085-0799View full profile → , Meghna Chhabrameghnachhabra1@gmail.comDepartment of ManagementDelhi School of BusinessPitampura, Delhi, 110034, India0000-0003-2841-411XView full profile → , Tapan Senapatimath.tapan@gmail.comDepartment of EngineeringSaveetha Institute of Medical and Technical SciencesChennai, Tamil Nadu, 602105, India0000-0003-0399-7486View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Dec 2025
- Published Online:
- 06 Aug 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JSMS-1682
- Pages:
- 875–892
Abstract
This paper applies a set of econometric tools to examine cross-border associations among major emerging equity markets. Since the COVID-19 outbreak disrupted stock markets (SkMs) across the globe, the study relies on structural break methods to assess how international linkages among the selected markets shifted around this shock. The empirical strategy combines the Chow breakpoint test, the Chow forecast test, the cointegration procedure developed by author and the Granger Causality (GC) test. The full sample is split into two sub-periods, Pre-COVID (June 1st, 2011 – March 10th, 2020) and COVID (March 11th, 2020 – July 31st, 2021). The results point to a robust long-run cointegrating relationship among the selected markets before the pandemic, one that is no longer detectable once the pandemic period is considered separately. These findings may be of practical value to investors, investment firms, and portfolio managers weighing diversification across these markets.
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References
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