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Assessing efficiency in prices and trading volumes of cryptocurrencies before and during the COVID-19 pandemic with fractal, chaos, and randomness: evidence from a large dataset

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  • Salim Lahmiri

    (Concordia University
    ESCA École de Management)

Abstract

This study examines the market efficiency in the prices and volumes of transactions of 41 cryptocurrencies. Specifically, the correlation dimension (CD), Lyapunov Exponent (LE), and approximate entropy (AE) were estimated before and during the COVID-19 pandemic. Then, we applied Student’s t-test and F-test to check whether the estimated nonlinear features differ across periods. The empirical results show that (i) the COVID-19 pandemic has not affected the means of CD, LE, and AE in prices, (ii) the variances of CD, LE, and AE estimated from prices are different across pre-pandemic and during pandemic periods, and specifically (iii) the variance of CD decreased during the pandemic; however, the variance of LE and the variance of AE increased during the pandemic period. Furthermore, the pandemic has not affected all three features estimated from the volume series. Our findings suggest that investing in cryptocurrencies is advantageous during a pandemic because their prices become more regular and stable, and the latter has not affected the volume of transactions.

Suggested Citation

  • Salim Lahmiri, 2024. "Assessing efficiency in prices and trading volumes of cryptocurrencies before and during the COVID-19 pandemic with fractal, chaos, and randomness: evidence from a large dataset," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-12, December.
  • Handle: RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-024-00628-0
    DOI: 10.1186/s40854-024-00628-0
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    1. Caporale, Guglielmo Maria & Kang, Woo-Young & Spagnolo, Fabio & Spagnolo, Nicola, 2022. "The COVID-19 pandemic, policy responses and stock markets in the G20," International Economics, Elsevier, vol. 172(C), pages 77-90.
    2. Li, Jingyu & Liu, Ranran & Yao, Yanzhen & Xie, Qiwei, 2022. "Time-frequency volatility spillovers across the international crude oil market and Chinese major energy futures markets: Evidence from COVID-19," Resources Policy, Elsevier, vol. 77(C).
    3. Christopher J McMahon & Joshua P Toomey & Deb M Kane, 2017. "Insights on correlation dimension from dynamics mapping of three experimental nonlinear laser systems," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-27, August.
    4. Liu, Xiaoxing & Shehzad, Khurram & Kocak, Emrah & Zaman, Umer, 2022. "Dynamic correlations and portfolio implications across stock and commodity markets before and during the COVID-19 era: A key role of gold," Resources Policy, Elsevier, vol. 79(C).
    5. Hsu, Yu-Lin & Tang, Leilei, 2022. "Effects of investor sentiment and country governance on unexpected conditional volatility during the COVID-19 pandemic: Evidence from global stock markets," International Review of Financial Analysis, Elsevier, vol. 82(C).
    6. Fousekis, Panos & Tzaferi, Dimitra, 2021. "Returns and volume: Frequency connectedness in cryptocurrency markets," Economic Modelling, Elsevier, vol. 95(C), pages 13-20.
    7. James, Nick & Menzies, Max & Chan, Jennifer, 2021. "Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    8. Aloui, Donia, 2021. "The COVID-19 pandemic haunting the transmission of the quantitative easing to the exchange rate," Finance Research Letters, Elsevier, vol. 43(C).
    9. Arouxet, M. Belén & Bariviera, Aurelio F. & Pastor, Verónica E. & Vampa, Victoria, 2022. "Covid-19 impact on cryptocurrencies: Evidence from a wavelet-based Hurst exponent," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    10. Sun, Yiguo & Li, Delong & Suo, Chenyi & Wang, Yu, 2023. "A threshold effect of COVID-19 risk on oil price returns," Energy Economics, Elsevier, vol. 120(C).
    11. Assaf, Ata & Kristoufek, Ladislav & Demir, Ender & Kumar Mitra, Subrata, 2021. "Market efficiency in the art markets using a combination of long memory, fractal dimension, and approximate entropy measures," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    12. Kyriazis, Nikolaos & Papadamou, Stephanos & Tzeremes, Panayiotis & Corbet, Shaen, 2023. "The differential influence of social media sentiment on cryptocurrency returns and volatility during COVID-19," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 307-317.
    13. Guven, Murat & Cetinguc, Basak & Guloglu, Bulent & Calisir, Fethi, 2022. "The effects of daily growth in COVID-19 deaths, cases, and governments’ response policies on stock markets of emerging economies," Research in International Business and Finance, Elsevier, vol. 61(C).
    14. Tsionas, Mike G. & Michaelides, Panayotis G., 2017. "Neglected chaos in international stock markets: Bayesian analysis of the joint return–volatility dynamical system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 95-107.
    15. Yarovaya, Larisa & Zięba, Damian, 2022. "Intraday volume-return nexus in cryptocurrency markets: Novel evidence from cryptocurrency classification," Research in International Business and Finance, Elsevier, vol. 60(C).
    16. Ain Shahrier, Nur, 2022. "Contagion effects in ASEAN-5 exchange rates during the Covid-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    17. Partida, Alberto & Gerassis, Saki & Criado, Regino & Romance, Miguel & Giráldez, Eduardo & Taboada, Javier, 2022. "The chaotic, self-similar and hierarchical patterns in Bitcoin and Ethereum price series," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    18. Salisu, Afees A. & Raheem, Ibrahim D. & Vo, Xuan Vinh, 2021. "Assessing the safe haven property of the gold market during COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 74(C).
    19. Wu, JunFeng & Zhang, Chao & Chen, Yun, 2022. "Analysis of risk correlations among stock markets during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 83(C).
    20. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2023. "Which COVID-19 information really impacts stock markets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    21. Nie, Chun-Xiao, 2017. "Correlation dimension of financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 632-639.
    22. Ahundjanov, Behzod B. & Akhundjanov, Sherzod B. & Okhunjanov, Botir B., 2021. "Risk perception and oil and gasoline markets under COVID-19," Journal of Economics and Business, Elsevier, vol. 115(C).
    23. Aquilante, Tommaso & Di Pace, Federico & Masolo, Riccardo M., 2022. "Exchange-rate and news: Evidence from the COVID pandemic," Economics Letters, Elsevier, vol. 213(C).
    24. Min Xu & Xingtong Chen & Gang Kou, 2019. "A systematic review of blockchain," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-14, December.
    25. Chen, Yufeng & Wang, Chuwen & Zhu, Zhitao, 2022. "Toward the integration of European gas futures market under COVID-19 shock: A quantile connectedness approach," Energy Economics, Elsevier, vol. 114(C).
    26. Thai Hung, Ngo & Nguyen, Linh Thi My & Vinh Vo, Xuan, 2022. "Exchange rate volatility connectedness during Covid-19 outbreak: DECO-GARCH and Transfer Entropy approaches," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    27. Jin, Lifu & Zheng, Bo & Ma, Jiahao & Zhang, Jiu & Xiong, Long & Jiang, Xiongfei & Li, Jiangcheng, 2022. "Empirical study and model simulation of global stock market dynamics during COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    28. Stosic, Darko & Stosic, Dusan & Ludermir, Teresa B. & Stosic, Tatijana, 2019. "Multifractal behavior of price and volume changes in the cryptocurrency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 54-61.
    29. Foroutan, Parisa & Lahmiri, Salim, 2022. "The effect of COVID-19 pandemic on return-volume and return-volatility relationships in cryptocurrency markets," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    30. Niu, Zibo & Ma, Feng & Zhang, Hongwei, 2022. "The role of uncertainty measures in volatility forecasting of the crude oil futures market before and during the COVID-19 pandemic," Energy Economics, Elsevier, vol. 112(C).
    31. Al-Shboul, Mohammad & Assaf, Ata & Mokni, Khaled, 2022. "When bitcoin lost its position: Cryptocurrency uncertainty and the dynamic spillover among cryptocurrencies before and during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 83(C).
    32. Panas, Epaminondas & Ninni, Vassilia, 2000. "Are oil markets chaotic? A non-linear dynamic analysis," Energy Economics, Elsevier, vol. 22(5), pages 549-568, October.
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