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<article article-type="Research Article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher">journal-of-statistics-and-management-systems</journal-id>
      <journal-title-group>
        <journal-title> Journal of Statistics and Management Systems</journal-title>
      </journal-title-group>
      <issn publication-format="electronic">2169-0014</issn>
      <issn publication-format="print">0972-0510</issn>
      <publisher>
        <publisher-name>Taru Publications</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.47974/JSMS-1682</article-id>
      <title-group>
        <article-title>Data-driven modeling of global financial linkages : Structural break analysis using computational intelligence</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Goel</surname>
            <given-names>Himanshu</given-names>
          </name>
          <aff>Department of Management, Jagan Institute of Management Studies, Rohini, Delhi, 110085, India</aff>
        </contrib>
        <contrib contrib-type="author" corresp="yes">
          <name>
            <surname>Agarwal</surname>
            <given-names>Monika</given-names>
          </name>
          <aff>Department of Management, Jagan Institute of Management Studies, Rohini, Delhi, 110085, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Chhabra</surname>
            <given-names>Meghna</given-names>
          </name>
          <aff>Department of Management, Delhi School of Business, Pitampura, Delhi, 110034, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Senapati</surname>
            <given-names>Tapan</given-names>
          </name>
          <aff>Department of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, 602105, India</aff>
        </contrib>
      </contrib-group>
      <volume>29</volume>
      <issue>7 &amp; 8 July &amp; August</issue>
      <fpage>875</fpage>
      <lpage>892</lpage>
      <pub-date date-type="pub">
        <day>06</day>
        <month>08</month>
        <year>2026</year>
      </pub-date>
      <abstract>
        <p>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.</p>
      </abstract>
      <kwd-group>
        <kwd>COVID-19</kwd>
        <kwd>Structural break analysis</kwd>
        <kwd>Emerging markets</kwd>
        <kwd>International linkages</kwd>
      </kwd-group>
      <custom-meta-group>
        <custom-meta>
          <meta-name>access</meta-name>
          <meta-value>open</meta-value>
        </custom-meta>
        <custom-meta>
          <meta-name>retracted</meta-name>
          <meta-value>no</meta-value>
        </custom-meta>
      </custom-meta-group>
    </article-meta>
  </front>
</article>
