THE EFFECTS OF COVID-19 ON MULTIFRACTALITY AND LONG-MEMORY IN ETHEREUM’S RETURNS

Jelena Radojičić, Ognjen Radovic

DOI Number
https://doi.org/10.22190/TEME221228014R
First page
Last page

Abstract


The global COVID-19 pandemic has shaken the global economy, not sparing the cryptocurrency market. In this paper, we investigate the impact of the COVID-19 pandemic on the dynamics of log returns of the Ethereum. The observed period is divided into three parts: the pre-pandemic period, the pandemic-induced shock, and the period after the pandemic-induced shock on the cryptocurrency market. The research focuses on the impact of the pandemic on the degree of non-linearity and multifractality of log returns. To assess the degree of non-linearity, we used the BDS test and the value of the largest Lyapunov exponent. For multifractality, long-range correlations and information efficiency, we used MF-DFA (Multifractal Detrended Fluctuation Analysis). The research results show that all observed periods have a pronounced non-linearity, but that there is no evidence of the existence of low-dimension chaos. Also, based on the results of the MF-DFA analysis, we conclude that the COVID-19 pandemic has significantly affected the long memory of the log returns of the Ethereum; however, their dynamics and characteristics are returning to the trends present before the pandemic.


Keywords

COVID-19, cryptocurrency market, multifractality, chaos, market efficiency

Full Text:

PDF

References


Assaf, A., Bhandari, A., Charif, H., & Demir, E. (2022) Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19. International Review of Financial Analysis, 82, 102132. doi: 10.1016/j.irfa.2022.102132.

Brock, WA., Dechert, D., Lebaron, B., & Scheinkman , J. (1996). A test for independence based on a correlation dimension, Econometric Review, 15, 197-235.

Cheng, Q., Liu, X., & Zhu, X. (2019). Cryptocurrency momentum effect: DFA and MF-DFA analysis. Physica A: Statistical Mechanics and its Applications, 526, 120847. doi:10.1016/j.physa.2019.04.083

Coinmarketcap (2022). Global Cryptocurrency Charts, Total Cryptocurrency MarketCap. Retrieved from https://coinmarketcap.com/charts/.

Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019) Cryptocurrencies as a financial asset: a systematic analysis. International Review of Financial Analysis, 62, 182-199. doi:10.1016/ j.irfa.2018.09.003.

Danylchuk, H., Kibalnyk, L., Kovtun, O., Kiv, A., Pursky, O., & Berezhna, G. (2020). Modelling of cryptocurrency market using fractal and entropy analysis in COVID-19, CEUR Workshop Proceedings 2713 (2020) 352–371. https://doi.org/10.31812/123456789/4477.

Diaconaşu D-E, Mehdian S, & Stoica O. (2022) An analysis of investors’ behavior in Bitcoin market. PLoS ONE, 17(3): e0264522. doi: 10.1371/journal.pone.0264522.

Elliott D. J., & de Lima, L. (2018). Crypto-assets: their future and regulation. Oliver Wyman, October. Retrieved from https://www.atlantafed.org/-/media/documents/news/conferences/2018/1018-financial-stability-implications-of-new-technology/papers/elliott_crypto-assets.pdf.

Gencay, R., and Dechert, WD., 1992. An algorithm for the n Lyapunov exponents of an n-dimentional unknown dynamical system, Phisica D, 59,142-157.

Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M. C. and Siering, M. (2014), Bitcoinasset or currency? Revealing users’ hidden intentions, in: Avital, M., Leimeister, M. and Schultze, U. (eds), Proceedings of the European conference on information systems, Israel: Tel Aviv, pp. 1-14.

Goodell, J.W. (2020). COVID-19 and finance: agendas for future research. Finance Research Letters, 35, 101512. doi:10.1016/j.frl.2020.101512.

CryptoDataDownload (2022) Coinbase. https://www.cryptodatadownload.com/data/coinbase/.

Gu, R., Shaob, Y., & Wang, Q. (2013) Physica A 392, 361–370.

Gunay, S., & Kaşkaloğlu, K. (2019) Seeking a Chaotic Order in the Cryptocurrency Market. Mathematical and Computational Applications, 24(2), 36. doi: 10.3390/mca24020036.

Hurst, H. E. (1965). Long-term storage: An experimental study. London: Constable.

Ihlen, E. (2012). Introduction to multifractal detrended fluctuation analysis in matlab. Frontiers in Physiology. Sec. Fractal Physiology, 3. doi:10.3389/fphys.2012.00141

Kakinaka S., & Umeno K. (2022) Cryptocurrency market efficiency in short- and long-term horizons during COVID-19: An asymmetric multifractal analysis approach. Finance Research Letters, 46, 102319. doi: 10.1016/j.frl.2021.102319.

Kantelhardt, J. W., Zschiegner, S. A., Koscielny-Bunde, E., Havlin, S., Bunde, A., & Stanley, H. E. (2002). Multifractal detrended fluctuation analysis of nonstationary time series. Physica A, 316, 87–114.

Lahmiri, S., & Bekiros, S. (2021). The effect of COVID-19 on long memory in returns and volatility of cryptocurrency and stock markets. Chaos, Solitons & Fractals, 151, 111221. doi: 10.1016/j.chaos.2021.111221.

Mnif, E., Jarboui, A., & Mouakhar K. (2020). How the cryptocurrency market has performed during COVID 19? A multifractal analysis. Finance Research Letters, 36, 101647. doi:10.1016/j.frl.2020.101647.

Mnif, E., Salhi, B, Trabelsi, L., & Jarboui, A. (2022) Efficiency and herding analysis in gold-backed cryptocurrencies. Heliyon, 8 (12), e11982. doi: 10.1016/j.heliyon.2022.e11982.

Mohammadi, Shapour, (2020a), LYAPROSEN: MATLAB function to calculate Lyapunov exponent; Boston College Department of Economics: Boston, MA, USA.

Mohammadi, Shapour, (2020b), ANNLYAP: MATLAB function to calculate Lyapunov exponents; Boston College Department of Economics: Boston, MA, USA.

Naeem, M. A., Bouri, E., Peng, Z., Shahzad, S. J. H., & Vo, X. V. (2021). Asymmetric efficiency of cryptocurrencies during COVID19. Physica A: Statistical Mechanics and its Applications, 565, 125562.

Nakamoto, S. (2009). Bitcoin: A Peer-to-Peer Electronic Cash System. Retrieved from https://Bitcoin.org/en/Bitcoin-paper.

Tsallis, C. (1988). Possible generalization of Boltzmann–Gibbs statistics. Journal of Statistical Physics, 52, 1-2, 479–487, 1988.

Vidal-Tomás, D., Ibañez. A.M., & Farinós, J.E. (2019). Herding in the cryptocurrency market: CSSD and CSAD approaches. Finance research letters, 30, 181–186.

Wolf, A., Swift, J., Swinney, H., & Vastano, J. (1985). Determining Lyapunov exponents from a time series. Physica D, 16, 285-317.

Wu, X., Wu, L., & Chen, S. (2022). Long memory and efficiency of Bitcoin during COVID19. Applied Economics, 54(4), 375–389.




DOI: https://doi.org/10.22190/TEME221228014R

Refbacks

  • There are currently no refbacks.


© University of Niš, Serbia
Creative Commons licence CC BY-NC-ND
Print ISSN: 0353-7919
Online ISSN: 1820-7804