• Emna Mnif Sfax University, Tunisia
  • Anis Jarboui Sfax University, Tunisia
Keywords: Islamic cryptocurrencies, Covid-19, Efficiency, MFDFA


Unlike conventional cryptocurrencies, Islamic ones are new technologies backed by tangible assets and are characterised by their fundamental values. After the COVID-19 outbreak, cryptocurrency responses have shown different behaviour to stock market reactions. However, there is a lack of studies on the efficiency of Islamic and green cryptocurrencies during the pandemic. This paper attempts to analyse the behaviour of three typical families of cryptocurrencies (conventional, Islamic, and green) extracted according to their availability in daily frequencies during COVID-19. For this purpose, their efficiency levels are studied before and after the outbreak by employing multifractal detrended fluctuation analysis (MFDFA) to make the best predictions and strategies. The inefficiency of the cryptocurrencies is assessed through a magnitude of long-memory (MLM) efficiency index, and the impact of COVID-19 on their efficiency is evaluated. The primary results show that HelloGold was the most efficient market before the COVID-19 outbreak and that subsequently Ethereum has been the most efficient. In addition, the findings reveal that the cryptocurrency reactions are not similar and show more resilience in the Ethereum and Litecoin markets than in other cryptocurrency markets. The main contribution of this study is the evaluation of the impact of COVID-19 on the various classes of crypto money. This work has practical implications, as it provides new insights into trading opportunities and market reactions. Moreover,  he work has theoretical implications based on its evaluation of three distinct models from different doctrine viewpoints.


Aslam, F., Aziz, S., Nguyen, D. K., Mughal, K. S., & Khan, M. (2020). On the efficiency of foreign exchange markets in times of the COVID-19 pandemic. Technological Forecasting and Social Change, 161(December 2020), 120261.

Avery, C., & Zemsky, P. (1998). Multidimensional uncertainty and herd behavior in financial markets. American Economic Review, 88(4), 724-748.

Ballis, A., & Drakos, K. (2019). Testing for herding in the cryptocurrency market. Finance Research Letters, 33(March 2020), 101210.

Banerjee, A. V. (1992). A simple model of herd behavior. The Quarterly Journal of Economics, 107(3), 797-817.

Barunik, J., & Kristoufek, L. (2010). On hurst exponent estimation under heavytailed distributions. Physica A: Statistical Mechanics and Its Applications, 389(18), 3844-3855.

Benbachir, S., & El Alaoui, M. (2011). A multifractal detrended fluctuation analysis of the Moroccan stock exchange. International Research Journal of Finance and Economics, 78(2011), 6-17.

Berentsen, A., & Schär, F. (2018). A short introduction to the world of cryptocurrencies. Review, 100(1), 1-16.

Blanco Rivero, J. J. (2019). The fractal geometry of Luhmann’s sociological theory or debugging systems theory. Technological Forecasting and Social Change, 146(September 2019), 31-40.

Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015a). Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213-238.

Bouri, E., Gupta, R., & Roubaud, D. (2019). Herding behaviour in cryptocurrencies. Finance Research Letters, 29(June 2019), 216-221.

Calvet, L. E., & Fisher, A. J. (2008). Multifractual volatility: Theory, forecasting and pricing. In Monographs of the Society for Research in Child Development.

Celeste, V., Corbet, S., & Gurdgiev, C. (2020). Fractal dynamics and wavelet analysis: Deep volatility and return properties of Bitcoin, Ethereum and Ripple. Quarterly Review of Economics and Finance, 76(May 2020), 310-324. https://doi.


Chaffai, M., & Medhioub, I. (2018). Herding behavior in Islamic GCC stock market: A daily analysis. International Journal of Islamic and Middle Eastern Finance and Management, 11(2), 182-193.

Chen, T. (2013). Do investors herd in global stock markets? Journal of Behavioral Finance, 14(3), 230-239.

Chen, Y., & Yu, J. (2011). Efficient market hypothesis in the international oil price fluctuation: Based on the MF-DFA model. International Journal of Global Energy Issues, 35(2-4).

Choudhury, M. A. (2018). Micro-money, finance and real economy interrelationship in the framework of islamic ontology of unity of knowledge and the worldsystem of social economy. International Journal of Social Economics, 45(2), 445-

Christie, W. G., & Huang, R. D. (1995). Following the pied piper: Do individual returns herd around the market? Financial Analysts Journal, 51(4), 31-37.

Conlon, T., Corbet, S., & McGee, R. J. (2020). Are cryptocurrencies a safe haven for equity markets? An international perspective from the COVID-19 pandemic. Research in International Business and Finance, 54(December 2020), 101248.

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

Davies, T. W., Bennie, J., Inger, R., & Gaston, K. J. (2013). Artificial light alters natural regimes of night-time sky brightness. Scientific Reports, 3(1722), 1-6.

Fama, E. F. (1965). The Behavior of stock-market prices. The Journal of Business, 38(1), 34-105.

Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: Measuring stock market comovements. The Journal of Finance, 57(5), 2223-2261.

Fillol, J. (2005). Modélisation multifractale du taux de change dollar/euro. Economie Internationale, 2005/4(n0104), 135-150.

Ghosh, B., & Kozarevic, E. (2019). Multifractal analysis of volatility for detection of herding and bubble: Evidence from CNX Nifty HFT. Investment Management and Financial Innovations, 16(3), 182-193.

Harte, D. (2001). Multifractals: Theory and applications. Florida: Chapman & Hall/CRC.

Hausdorff, F. (1918). Dimension und äußeres Maß. Mathematische Annalen, 79(March 1918), 157-179.

Henker, J., Henker, T., & Mitsios, A. (2012). Do investors herd intraday in the Australian equities market? SSRN Electronic Journal. Availabe at :

Hu, Y., Valera, H. G. A., & Oxley, L. (2019). Market efficiency of the top market-cap cryptocurrencies: Further evidence from a panel framework. Finance Research Letters, 31(December 2019), 138-145.

Humayun Kabir, M., & Shakur, S. (2018). Regime-dependent herding behavior in Asian and Latin American stock markets. Pacific Basin Finance Journal, 47(February 2018), 60-78.

Hwang, S., & Salmon, M. (2004). Market stress and herding. Journal of Empirical Finance, 11(4), 585-616.

Ihlen, E. A. F. (2012). Introduction to multifractal detrended fluctuation analysis in Matlab. Frontiers in Physiology, 3(June 2012), 141.

Jacquet, P., & Mans, B. (2019). Green mining: Toward a less energetic impact of cryptocurrencies. INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019. Available at:


Ji, Q., Zhang, D., & Zhao, Y. (2020). Searching for safe-haven assets during the COVID-19 pandemic. International Review of Financial Analysis, 71(October 2020), 101526.

Jizba, P., & Arimitsu, T. (2004). The world according to Rényi: Thermodynamics of multifractal systems. Annals of Physics, 312(1), 17-59.

Jørgensen, B., & Kokonendji, C. C. (2011). Dispersion models for geometric sums. Brazilian Journal of Probability and Statistics, 25(3), 263-293.

Kaiser, L., & Stöckl, S. (2019). Cryptocurrencies: Herding and the transfer currency. Finance Research Letters, 33(March 2020), 101214.

Kantelhardt, J. W. (2009). Fractal and Multifractal Time Series. In: Meyers R. (eds) Encyclopedia of Complexity and Systems Science. New York: Springer

Kantelhardt, J. W. (2015). Fractal and Multifractal Time Series. In: Meyers R. (eds), Encyclopedia of Complexity and Systems Science. Berlin: Springer.

Keynes, J. M. (2017). The General theory of employment, interest and money. In Modern Economic Classics-Evaluations Through Time. Cham: Palgrave Macmillan.

Khuntia, S., & Pattanayak, J. K. (2020). Adaptive long memory in volatility of intraday bitcoin returns and the impact of trading volume. Finance Research Letters, 32(January 2020), 101077.

Kinsella, A., & Taylor, S. (1987). Modelling Financial Time Series. The Statistician, 36(4), 433.

Kristoufek, L. (2020). Grandpa, grandpa, tell me the one about Bitcoin being a safe haven: New evidence from the COVID-19 pandemic. Frontiers in Physics, 8, 296.

Kristoufek, L., & Vosvrda, M. (2019). Cryptocurrencies market efficiency ranking: Not so straightforward. Physica A: Statistical Mechanics and Its Applications, 531(October 2019), 120853.

Kukacka, J., & Kristoufek, L. (2019). Do complex financial models lead to complex dynamics? Agent-based models and multifractality. SSRN Electronic Journal.

Kukacka, J., & Kristoufek, L. (2020). Do ‘complex’ financial models really lead to complex dynamics? Agent-based models and multifractality. Journal of Economic Dynamics and Control, 113(April 2020), 103855. https://doi.


Lahmiri, S., & Bekiros, S. (2019). Decomposing the persistence structure of Islamic and green crypto-currencies with nonlinear stepwise filtering. Chaos, Solitons and Fractals, 127(October 2019), 334-341.


Lahmiri, S., & Bekiros, S. (2020). The impact of COVID-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets. Chaos, Solitons and Fractals, 138(September 2020), 109936.


Lahmiri, S., Bekiros, S., & Bezzina, F. (2020). Multi-fluctuation nonlinear patterns of European financial markets based on adaptive filtering with application to family business, green, Islamic, common stocks, and comparison with Bitcoin, NASDAQ, and VIX. Physica A: Statistical Mechanics and its Applications, 538(January 2020), 122858.

Lakonishok, J., Shleifer, A., & Vishny, R. W. (1992). The impact of institutional trading on stock prices. Journal of Financial Economics, 32(1), 23-43.

Lakshman, M. V., Basu, S., & Vaidyanathan, R. (2013). Market-wide herding and the impact of institutional investors in the Indian capital market. Journal of Emerging Market Finance, 12(2), 197-237.

Lashermes, B., Abry, P., & Chainais, P. (2004). New insights into the estimation of scaling exponents. International Journal of Wavelets, Multiresolution and Information Processing, 2(4), 497-523.

Lillo, F., Moro, E., Vaglica, G., & Mantegna, R. N. (2008). Specialization and herding behavior of trading firms in a financial market. New Journal of Physics, 10(April 2008).

López, J. L., & Contreras, J. G. (2013). Performance of multifractal detrended fluctuation analysis on short time series. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 87(February 2013), 022918.


Lu, X., Tian, J., Zhou, Y., & Li, Z. (2013). Multifractal detrended fluctuation analysis of the Chinese stock index futures market. Physica A: Statistical Mechanics and its Applications, 392(6), 1452-1458.

Mandelbrot, B. (1963). The variation of certain speculative prices. The Journal of Business, 36, 394-419.

Mandelbrot, B. B. (1975). Stochastic models for the earth’s relief, the shape and the fractal dimension of the coastlines, and the number area rule for islands. Proceedings of the National Academy of Sciences of the United States of America,

(10), 3825-3828.

Mandelbrot, B. B. (1997). The variation of certain speculative prices. In Fractals and Scaling in Finance (pp. 371-418). New York: Springer.

Meneveau, C., & Sreenivasan, K. R. (1987). The multifractal spectrum of the dissipation field in turbulent flows. Nuclear Physics B - Proceedings Supplements, 2(November 1987), 49-76.

Mensi, W., Hammoudeh, S., Al-Jarrah, I. M. W., Sensoy, A., & Kang, S. H. (2017). Dynamic risk spillovers between gold, oil prices and conventional, sustainability and Islamic equity aggregates and sectors with portfolio implications. Energy Economics, 67(September 2017), 454-475.

Mnif, E., Jarboui, A., & Mouakhar, K. (2020). How the cryptocurrency market has performed during COVID 19? A multifractal analysis. Finance Research Letters, 36(October 2020)., 101647.

Mnif, E., Salhi, B., & Jarboui, A. (2019). Herding behaviour and Islamic market efficiency assessment: Case of Dow Jones and sukuk market. International Journal of Islamic and Middle Eastern Finance and Management, 13(1), 24-41.

Movahed, M. S., Jafari, G. R., Ghasemi, F., Rahvar, S., & Tabar, M. R. R. (2006). Multifractal detrended fluctuation analysis of sunspot time series. Journal of Statistical Mechanics: Theory and Experiment, 2006(February). https://doi.


Narayan, P. K., Narayan, S., Popp, S., & D’Rosario, M. (2011). Share price clustering in Mexico. International Review of Financial Analysis, 20(2), 113-119.

Omar, M. N. (2011). The Islamic view on money and its implication to financial instruments. ISRA International Journal of Islamic Finance, 3(1), 161-168.

Orléan, A. (1992). Contagion des opinions et fonctionnement des marchés financiers. Revue Économique, 43(4), 685-697.

Scharfstein, D. S., & Stein, J. C. (1990). Herd behavior and investment. American Economic Review, 80(3), 465-479.

Schwarzenberger, R., & Falconer, K. (1990). Fractal geometry: Mathematical foundations and applications. The Mathematical Gazette, 74(469), 316-317.

Siswantoro, D., Handika, R., & Mita, A. F. (2020). The requirements of cryptocurrency for money, an Islamic view. Heliyon, 6(1), e03235.

Sorwar, G., Pappas, V., Pereira, J., & Nurullah, M. (2016). To debt or not to debt: Are Islamic banks less risky than conventional banks? Journal of Economic Behavior and Organization, 132(Supplement), 113-126.

Stavroyiannis, S., & Babalos, V. (2019). Time-varying herding behavior within the Eurozone stock markets during crisis periods: Novel evidence from a TVP model. Review of Behavioral Finance, 12(2), 83-96.

Struzik, Z. R. (1999). Local effective hölder exponent estimation on the wavelet transform maxima tree. In Fractals (pp. 93-112). Switzerland: Springer Link

Takaishi, T. (2020). Rough volatility of Bitcoin. Finance Research Letters, 32(January 2020), 101379.

Tiwari, A. K., Aye, G. C., & Gupta, R. (2019). Stock market efficiency analysis using long spans of Data: A multifractal detrended fluctuation approach. Finance Research Letters, 28(March 2019), 398-411.


Vidal-Tomás, D., Ibáñez, A. M., & Farinós, J. E. (2019). Herding in the cryptocurrency market: CSSD and CSAD approaches. Finance Research Letters, 30(September 2019), 181-186.

Wei, W. C. (2018). Liquidity and market efficiency in cryptocurrencies. Economics Letters, 168(July 2018), 21-24.

Wilson, R. (2019). Implications of technological advance for financial intermediation in Islamic finance. In U. A Oseni, & S. N, Ali (Eds.), Fintech in Islamic Finance; Theory and Practice (pp. 33-46). Abingdon, Oxon: Routledge. https://doi.


Winsor, R. D. (1995). Marketing under conditions of chaos. Percolation metaphors and models. Journal of Business Research, 34(3), 181-189.

Yarovaya, L., Matkovskyy, R., & Jalan, A. (2020). The Effects of a “Black Swan” event (COVID-19) on herding behavior in cryptocurrency markets: Evidence from cryptocurrency USD, EUR, JPY and KRW markets. SSRN Electronic Journal (April 27, 2020).

How to Cite
Mnif, E., & Jarboui, A. (2021). ISLAMIC, GREEN, AND CONVENTIONAL CRYPTOCURRENCY MARKET EFFICIENCY DURING THE COVID-19 PANDEMIC. Journal of Islamic Monetary Economics and Finance, 7, 167 - 184.