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Dynamic connectedness among market volatilities: a perspective of COVID-19 and Russia-Ukraine conflict

Author

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  • Prince Kumar Maurya
  • Rohit Bansal
  • Anand Kumar Mishra

Abstract

Purpose - This paper aims to investigate the dynamic volatility connectedness among 13 G20 countries by using the volatility indices. Design/methodology/approach - The connectedness approach based on the time-varying parameter vector autoregression model has been used to investigate the linkage. The period of study is from 1 January 2014 to 20 April 2023. Findings - This analysis revealed that volatility connectedness among the countries during COVID-19 and Russia–Ukraine conflict had increased significantly. Furthermore, analysis has indicated that investors had not anticipated the World Health Organization announcement of COVID-19 as a global pandemic. Contrarily, investors had anticipated the Russian invasion of Ukraine, evident in a significant rise in volatility before and after the invasion. In addition, the transmission of volatility is from developed to developing countries. Developed countries are NET volatility transmitters, whereas developing countries are NET volatility receivers. Finally, the ordinary least square regression result suggests that the volatility connectedness index is informative of stock market dynamics. Originality/value - The connectedness approach has been widely used to estimate the dynamic connectedness among market indices, cryptocurrencies, sectoral indices, enegy commodities and metals. To the best of the authors’ knowledge, none of the previous studies have directly used the volatility indices to measure the volatility connectedness. Hence, this study is the first of its kind that has used volatility indices to measure the volatility connectedness among the countries.

Suggested Citation

  • Prince Kumar Maurya & Rohit Bansal & Anand Kumar Mishra, 2024. "Dynamic connectedness among market volatilities: a perspective of COVID-19 and Russia-Ukraine conflict," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 41(5), pages 1119-1140, April.
  • Handle: RePEc:eme:sefpps:sef-01-2024-0029
    DOI: 10.1108/SEF-01-2024-0029
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    More about this item

    Keywords

    Stock market; Volatility connectedness; Volatility spillover; TVP-VAR; COVID-19; Russia–Ukraine conflict; C32; C55; C58; 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
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • F36 - International Economics - - International Finance - - - Financial Aspects of Economic Integration
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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