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Do bitcoins follow a random walk model?

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  • Aggarwal, Divya

Abstract

Bitcoins have become a fad among investors despite of the ambiguity surrounding on its nature and characteristics. This study aims to contribute to the existing literature of examining bitcoin returns under a financial asset purview. Through multiple robust tests, the market efficiency of daily bitcoin returns is analyzed for the time frame of July 2010 till March 2018. Strong evidence of market inefficiency characterized by absence of random walk model is found. The market inefficiency was found attributable to the presence of asymmetric volatility clustering. More studies are needed to examine the temporal dynamics of bitcoin returns.

Suggested Citation

  • Aggarwal, Divya, 2019. "Do bitcoins follow a random walk model?," Research in Economics, Elsevier, vol. 73(1), pages 15-22.
    • Handle: RePEc:eee:reecon:v:73:y:2019:i:1:p:15-22
    DOI: 10.1016/j.rie.2019.01.002
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    Cited by:

    1. Bouri, Elie & Roubaud, David & Shahzad, Syed Jawad Hussain, 2020. "Do Bitcoin and other cryptocurrencies jump together?," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 396-409.
    2. Bouri, Elie & Vo, Xuan Vinh & Saeed, Tareq, 2021. "Return equicorrelation in the cryptocurrency market: Analysis and determinants," Finance Research Letters, Elsevier, vol. 38(C).
    3. Fung, Kennard & Jeong, Jiin & Pereira, Javier, 2022. "More to cryptos than bitcoin: A GARCH modelling of heterogeneous cryptocurrencies," Finance Research Letters, Elsevier, vol. 47(PA).
    4. Ma, Yu & Luan, Zhiqian, 2022. "Ethereum synchronicity, upside volatility and Bitcoin crash risk," Finance Research Letters, Elsevier, vol. 46(PA).
    5. Duan, Kun & Li, Zeming & Urquhart, Andrew & Ye, Jinqiang, 2021. "Dynamic efficiency and arbitrage potential in Bitcoin: A long-memory approach," International Review of Financial Analysis, Elsevier, vol. 75(C).
    6. Rasim Özcan & Asad ul Islam KHAN & Sundas Iftikhar, 2024. "Whether The Cr Whether The Crypto Market Is Efficient? E et Is Efficient? Evidence F vidence From Testing The Validity Of The Efficient Market Hypothesis," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 27(1), pages 113-132, March.
    7. Duan, Kun & Zhang, Liya & Urquhart, Andrew & Yao, Kai & Peng, Long, 2024. "Do clean and dirty cryptocurrencies connect financial assets differently? The perspective of market inefficiency," Research in International Business and Finance, Elsevier, vol. 70(PB).
    8. Apopo, Natalay & Phiri, Andrew, 2019. "On the (in)efficiency of cryptocurrencies: Have they taken daily or weekly random walks?," MPRA Paper 94712, University Library of Munich, Germany.
    9. Rico-Peña, Juan Jesús & Arguedas-Sanz, Raquel & López-Martin, Carmen, 2023. "Models used to characterise blockchain features. A systematic literature review and bibliometric analysis," Technovation, Elsevier, vol. 123(C).
    10. Hyeonoh Kim & Eojin Yi & Jooyoung Jeon & Taeyoung Park & Kwangwon Ahn, 2024. "After the Split: Market Efficiency of Bitcoin Cash," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 411-427, July.
    11. Guglielmo Maria Caporale & Alex Plastun & Viktor Oliinyk, 2019. "Bitcoin fluctuations and the frequency of price overreactions," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(2), pages 109-131, June.
    12. Samia Nasreen & Aviral Kumar Tiwari & Seong-Min Yoon, 2021. "Dynamic Connectedness and Portfolio Diversification during the Coronavirus Disease 2019 Pandemic: Evidence from the Cryptocurrency Market," Sustainability, MDPI, vol. 13(14), pages 1-14, July.
    13. Dias, Ishanka K. & Fernando, J.M. Ruwani & Fernando, P. Narada D., 2022. "Does investor sentiment predict bitcoin return and volatility? A quantile regression approach," International Review of Financial Analysis, Elsevier, vol. 84(C).
    14. Leonardo Ieracitano Vieira & Márcio Poletti Laurini, 2023. "Time-varying higher moments in Bitcoin," Digital Finance, Springer, vol. 5(2), pages 231-260, June.
    15. Pradipta Kumar Sahoo & Dinabandhu Sethi, 2024. "Market efficiency of the cryptocurrencies: Some new evidence based on price–volume relationship," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 1569-1580, April.
    16. Pho, Kim Hung & Ly, Sel & Lu, Richard & Hoang, Thi Hong Van & Wong, Wing-Keung, 2021. "Is Bitcoin a better portfolio diversifier than gold? A copula and sectoral analysis for China," International Review of Financial Analysis, Elsevier, vol. 74(C).
    17. Mingbo Zheng & Gen-Fu Feng & Xinxin Zhao & Chun-Ping Chang, 2023. "The transaction behavior of cryptocurrency and electricity consumption," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-18, December.
    18. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    19. Andrew Phiri, 2022. "Can wavelets produce a clearer picture of weak-form market efficiency in Bitcoin?," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 373-386, September.
    20. Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    21. Carmen López-Martín & Sonia Benito Muela & Raquel Arguedas, 2021. "Efficiency in cryptocurrency markets: new evidence," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 403-431, September.
    22. Praveen Kumar Tripathi & Manika Agarwal, 2024. "A Bayes Analysis of Random Walk Model Under Different Error Assumptions," Annals of Data Science, Springer, vol. 11(5), pages 1635-1652, October.
    23. Monica Alexiadou & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2023. "Cryptocurrencies and Long-Range Trends," IJFS, MDPI, vol. 11(1), pages 1-17, February.
    24. Constandina Koki & Stefanos Leonardos & Georgios Piliouras, 2019. "A Peek into the Unobservable: Hidden States and Bayesian Inference for the Bitcoin and Ether Price Series," Papers 1909.10957, arXiv.org, revised Jul 2021.

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    More about this item

    Keywords

    Bitcoin; Cryptocurrency; Random walk model; Auto regressive conditional heteroscedasticity; Volatility; Asymmetric;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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