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Return and volatility spillover between India and leading Asian and global equity markets: an empirical analysis

Author

Listed:
  • Aswini Kumar Mishra
  • Saksham Agrawal
  • Jash Ashish Patwa

Abstract

Purpose - The study uses the multivariate GARCH-BEKK model (which was first proposed by Babaet al. (1990) and then further developed by Engle and Kroner (1995)) to examine the return and volatility spillover between India and four leading Asian (namely, China, Japan, Singapore and Hong Kong) and two global (namely, the United Kingdom and the United States) equity markets. Design/methodology/approach - The study employs a multivariate GARCH-BEKK model to quantify return correlation and volatility transmission across the pre- and post-2008 global financial crisis periods (apart from other conventional time series modelling like cointegration, Granger causality using vector error correction model (VECM)). Findings - The results show a tendency of the Indian stock market index to move along with the US and Hong Kong market indices. The decrease in the value of the co-integration coefficient during the recession was explained by reduced investor confidence in developing countries. The result further shows a clear distinction in terms of volatility spillover between the Asian market vis-a-vis US and UK markets. Volatility transmission from India to Asian markets was found to be significantly higher as compared to the US and UK. So also, the study’s results show a puzzling result giving us comparable co-integration ranks for phase 2 (expansion) and phase 3 (slow-down) of the business cycle in most cases. Research limitations/implications - In Granger causality testing, the results were unable to ascertain the difference between phase 2 (expansion) and phase 3 (slowdown). However, the multivariate GARCH (MGARCH)-BEKK model showed a clear reduction in volatility transmission to NIFTY50 (is the flagship index on the National Stock Exchange of India Ltd. (NSE)) as India entered slow-down. This shows that the Indian economy does go through different business cycles, and the changes in parameters hence prove hypothesis 3 to be true with respect to volatility transmission to India from International markets. Originality/value - The results show that for all countries, the volatility transmitted to India increases significantly going from phase 1 (recession) to phase 2 (expansion) and reduces again once the countries enter slow-down in phase 3 (slowdown). This shows that during expansion shocks and impulses in international markets affect the Indian markets significantly, supporting the increase in co-integration in phase 2 (expansion). During expansion, developing markets like India become profitable for investors, due to the high growth rate when compared to developed countries. This implies that a significant amount of capital enters Indian markets, which is susceptible to the volatility of international markets. The volatility transmission from India to the US and UK was insignificant in phase 1 (recession and recovery) and phase 3 (slow-down) showing a weak linkage between the markets during volatile time periods.

Suggested Citation

  • Aswini Kumar Mishra & Saksham Agrawal & Jash Ashish Patwa, 2022. "Return and volatility spillover between India and leading Asian and global equity markets: an empirical analysis," Journal of Economics, Finance and Administrative Science, Emerald Group Publishing Limited, vol. 27(54), pages 294-312, May.
  • Handle: RePEc:eme:jefasp:jefas-06-2021-0082
    DOI: 10.1108/JEFAS-06-2021-0082
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    Citations

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    Cited by:

    1. Mishra, Aswini Kumar & Arunachalam, Vairam & Olson, Dennis & Patnaik, Debasis, 2023. "Dynamic connectedness in commodity futures markets during Covid-19 in India: New evidence from a TVP-VAR extended joint connectedness approach," Resources Policy, Elsevier, vol. 82(C).
    2. Harshit GURANI, 2023. "Predicting Stock Performance in Indian Mid-Cap and Small-Cap Firms: An Exploration of Financial Ratios Through Logistic Regression Analysis," CECCAR Business Review, Body of Expert and Licensed Accountants of Romania (CECCAR), vol. 4(9), pages 56-63, September.
    3. Aswini Kumar Mishra & Anand Theertha Nakhate & Yash Bagra & Abinash Singh & Bibhu Prasad Kar, 2024. "The Impact of Directional Global Economic Policy Uncertainty on Indian Stock Market Volatility: New Evidence," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 31(3), pages 423-452, September.
    4. Anastasia O. Volodina & Marina B. Trachenko, 2023. "ESG Investment Profitability in Developed and Emerging Markets with Regard to the Time Horizon," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 2, pages 59-73, April.

    More about this item

    Keywords

    Equity markets; Return spillover; Volatility spillover; GARCH-BEKK model; Business cycle; Investor behaviour; C32; F36; G15;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • F36 - International Economics - - International Finance - - - Financial Aspects of Economic Integration
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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