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Investment in Virtual Digital Assets Vis-A-Vis Equity Stock and Commodity: A Post-Covid Volatility Analysis

Author

Listed:
  • Nishi Sharma

    (University Institute of Applied Management Sciences, Panjab University, Chandigarh, India)

  • Shailika Rawat

    (University Institute of Applied Management Sciences, Panjab University, Chandigarh, India)

  • Arshdeep Kaur

    (Department of Commerce, Mehr Chand Mahajan DAV College for Women, Chandigarh, India)

Abstract

Virtual digital assets including cryptocurrencies, non-fungible tokens and decentralized financial asset have been initially used as an alternative currency but are currently being purchased as an asset and hedging instruments. Exponentially growing trading volume witnesses the growing inclination of investors towards these assets, and this calls for volatility analysis of these assets. In this reference, the present study assessed and compared the volatility of returns from investment in virtual digital assets, equity and commodity market. Daily closing prices of selected cryptocurrencies, non-fungible tokens and decentralized financial assets, stock indices and commodities have been analysed for the post-covid period. Since returns were observed to be heteroscedastic, autoregressive conditional heteroscedastic models have been used to assess the volatility. The results indicate a low correlation of commodity investment with all other investment opportunities. Also, Tether and Dai have been observed to be negatively correlated with stock market. This indicates the possibility of minimizing risk through portfolio diversification. In terms of average returns, virtual digital assets are discerned to be better options than equity stock or commodity yet the variance scenario of these investment avenues is not very rosy. The volatility parameters reveal that unlike commodity market, virtual digital assets have got a significant impact of external shocks in the short-run. Further, the long run persistency of shocks is observed to be higher for the UK stock market, followed by Ethereum, Tether and Dai. The present analysis is crucial as the decision about its acceptance as legal tender money is still sub-judice in some countries. The results are expected to provide insight to regulatory bodies about these assets.

Suggested Citation

  • Nishi Sharma & Shailika Rawat & Arshdeep Kaur, 2022. "Investment in Virtual Digital Assets Vis-A-Vis Equity Stock and Commodity: A Post-Covid Volatility Analysis," Virtual Economics, The London Academy of Science and Business, vol. 5(2), pages 95-113, September.
  • Handle: RePEc:aid:journl:v:5:y:2022:i:2:p:95-113
    DOI: 10.34021/ve.2022.05.02(5)
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    References listed on IDEAS

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    1. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    2. Marie Briere & Kim Oosterlinck & Ariane Szafarz, 2015. "Virtual Currency, Tangible Return: Portfolio Diversification with Bitcoins," Post-Print CEB, ULB -- Universite Libre de Bruxelles, vol. 16(6), pages 365-373.
    3. HaiYue Liu & Aqsa Manzoor & CangYu Wang & Lei Zhang & Zaira Manzoor, 2020. "The COVID-19 Outbreak and Affected Countries Stock Markets Response," IJERPH, MDPI, vol. 17(8), pages 1-19, April.
    4. Ji, Qiang & Zhang, Dayong & Zhao, Yuqian, 2020. "Searching for safe-haven assets during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 71(C).
    5. Tomaso Aste, 2019. "Cryptocurrency market structure: connecting emotions and economics," Papers 1903.00472, arXiv.org.
    6. Muhammad Ali Nasir & Toan Luu Duc Huynh & Sang Phu Nguyen & Duy Duong, 2019. "Forecasting cryptocurrency returns and volume using search engines," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-13, December.
    7. Corbet, Shaen & Lucey, Brian & Urquhart, Andrew & Yarovaya, Larisa, 2019. "Cryptocurrencies as a financial asset: A systematic analysis," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 182-199.
    8. López-Cabarcos, M. Ángeles & Pérez-Pico, Ada M. & Piñeiro-Chousa, Juan & Šević, Aleksandar, 2021. "Bitcoin volatility, stock market and investor sentiment. Are they connected?," Finance Research Letters, Elsevier, vol. 38(C).
    9. Zhang, Dayong & Hu, Min & Ji, Qiang, 2020. "Financial markets under the global pandemic of COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    10. Lynn Batten & Xun Yi, 2019. "Off-line digital cash schemes providing untraceability, anonymity and change," Electronic Commerce Research, Springer, vol. 19(1), pages 81-110, March.
    11. Tomaso Aste, 2019. "Cryptocurrency market structure: connecting emotions and economics," Digital Finance, Springer, vol. 1(1), pages 5-21, November.
    12. Thu, Le Ha & Leon-Gonzalez, Roberto, 2021. "Forecasting macroeconomic variables in emerging economies," Journal of Asian Economics, Elsevier, vol. 77(C).
    13. C. Baek & M. Elbeck, 2015. "Bitcoins as an investment or speculative vehicle? A first look," Applied Economics Letters, Taylor & Francis Journals, vol. 22(1), pages 30-34, January.
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