JOURNAL ARTICLE

Analyzing Spillover Effects Among BRICS Stock Markets: Application of Copula and DCC-MGARCH Model.

  • Published In: Review of Pacific Basin Financial Markets & Policies, 2023, v. 26, n. 4. P. 1 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Tripathy, Naliniprava; Panda, Pradiptarathi 3 of 3

Abstract

This study examines the nonlinear dependence and tail dependence of BRICS countries' stock markets and the contagion effect among Brazil, Russia, India, China, and South Africa (BRICS) countries' daily stock markets using the COPULA model from January 2000 to February 2019. The study employs the DCC-MGARCH model and Diebold and Yilmaz volatility spillover model to assess the interdependence dynamics across BRICS countries' stock markets. The copula results suggest that the BRICS country's stock markets are independent of each other. The conditional correlation between BRICS is negative and statistically significant, suggesting that the negative relationship among BRICS is an important signal for international investors to diversify among these countries and get the economic value of their investment. Further, Brazil, China, and South Africa are the net volatility transmitter, at the same time India and Russia are the net volatility receiver during the study period. The study proposes that policymaker of BRICS needs to interchange views and mutually map policies to appeal to global investment more. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Review of Pacific Basin Financial Markets & Policies. 2023/12, Vol. 26, Issue 4, p1
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2023
  • ISSN:0219-0915
  • DOI:10.1142/S0219091523500236
  • Accession Number:174622345
  • Copyright Statement:Copyright of Review of Pacific Basin Financial Markets & Policies is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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