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COVID-19’s disasters are perilous than Global Financial Crisis: A rumor or fact?

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  • Shehzad, Khurram
  • Xiaoxing, Liu
  • Kazouz, Hayfa

Abstract

This investigation employed the Asymmetric Power GARCH model and found that COVID-19 substantially harms the US and Japan's market returns. Moreover, COVID-19 has influenced the variance of the US, Germany, and Italy's stock markets more than the Global Financial Crises (GFC). However, GFC indicated a more significant impact on the financial volatility of the Nikkei 225 index and SSEC than COVID-19. The study confirmed the leverage effect for the S&P 500, Nasdaq Composite Index, DAX 30, Nikkei 225, FTSE MIB, and SSEC. The analysis authenticated that the health crisis that befell due to COVID-19 have imperatively originated the financial crisis globally; however, the Asian markets still make available better prospects for portfolio optimization.

Suggested Citation

  • Shehzad, Khurram & Xiaoxing, Liu & Kazouz, Hayfa, 2020. "COVID-19’s disasters are perilous than Global Financial Crisis: A rumor or fact?," Finance Research Letters, Elsevier, vol. 36(C).
  • Handle: RePEc:eee:finlet:v:36:y:2020:i:c:s1544612320305249
    DOI: 10.1016/j.frl.2020.101669
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    References listed on IDEAS

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    1. Sylvain Leduc & Zheng Liu, 2020. "The Uncertainty Channel of the Coronavirus," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, vol. 2020(07), pages 1-05, March.
    2. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    3. Kiymaz, Halil & Berument, Hakan, 2003. "The day of the week effect on stock market volatility and volume: International evidence," Review of Financial Economics, Elsevier, vol. 12(4), pages 363-380.
    4. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    5. Wen Hai & Zhong Zhao & Jian Wang & Zhen-Gang Hou, 2004. "The Short-Term Impact of SARS on the Chinese Economy," Asian Economic Papers, MIT Press, vol. 3(1), pages 57-61.
    6. Lidija Dedi & Burhan F. Yavas, 2016. "Return and volatility spillovers in equity markets: An investigation using various GARCH methodologies," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1266788-126, December.
    7. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    COVID-19; Global Financial Crises; APGARCH model; Financial markets; Leverage effect;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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