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The Impact of COVID-19 and the Russia-Ukraine Conflict on the Relationship Between the US Islamic Stock Index, Bitcoin, and Commodities

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  • Ahmad Monir Abdullah

    (Universiti Kebangsaan, Malaysia)

Abstract

In this article, the MGARCH-DCC model is utilised to compare the usefulness of Bitcoin, gold, and crude oil as a hedge and safe haven for the US Islamic stock index. We utilised daily data from August 2014 to April 2022, which covers the most recent COVID-19 epidemic and the Russia-Ukraine conflict. We find the dynamic correlation between Bitcoin and the US Islamic stock index to be low and often negative during major economic and political events, showing that Bitcoin is a safe haven and hedging instrument, especially during the pandemic period. However, we find that Bitcoin is very volatile, limiting its use as a safe haven and hedging instrument compared to gold. Gold is more stable and negatively correlated with the US Islamic stock index, making it more appropriate as a diversifier and hedging instrument. Adding gold to the US Islamic stock index portfolio reduces the portfolio’s risk.

Suggested Citation

  • Ahmad Monir Abdullah, 2023. "The Impact of COVID-19 and the Russia-Ukraine Conflict on the Relationship Between the US Islamic Stock Index, Bitcoin, and Commodities," Asian Economics Letters, Asia-Pacific Applied Economics Association, vol. 4(2), pages 1-6.
    • Handle: RePEc:ayb:jrnael:88
    DOI: 2023/06/12
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    References listed on IDEAS

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    1. Chkili, Walid, 2016. "Dynamic correlations and hedging effectiveness between gold and stock markets: Evidence for BRICS countries," Research in International Business and Finance, Elsevier, vol. 38(C), pages 22-34.
    2. Ratner, Mitchell & Chiu, Chih-Chieh (Jason), 2013. "Hedging stock sector risk with credit default swaps," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 18-25.
    3. Akkoc, Ugur & Civcir, Irfan, 2019. "Dynamic linkages between strategic commodities and stock market in Turkey: Evidence from SVAR-DCC-GARCH model," Resources Policy, Elsevier, vol. 62(C), pages 231-239.
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    Cited by:

    1. Yi Fang & Qirui Tang & Yanru Wang, 2024. "Geopolitical Risk and Cryptocurrency Market Volatility," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 60(14), pages 3254-3270, November.
    2. Long, Han & Prasad, Biman & Krishna, Victor & Tang, Kai & Chang, Chun-Ping, 2024. "Understanding the key determinants of Fiji's renewable energy," Economic Analysis and Policy, Elsevier, vol. 82(C), pages 1144-1157.

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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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