Revisiting the dynamics of major cryptocurrencies
Abstract
Purpose – This study aims to reassess the dynamics of major cryptocurrencies sur-rounding recent economic and geopolitical events. By employing wavelet analysis and quantile regression methods, it seeks to understand the behavior of cryptocurrencies before, during, and after the COVID-19 pandemic.
Research methodology – This research employs the Least Asymmetric Daubechies (LA8) wavelet function to decompose log-returns of major cryptocurrencies into various frequency scales. Additionally, it utilizes wavelet coherence and quantile-on-quantile regression techniques to analyze daily price data spanning from July 2017 to May 2024.
Findings – The findings reveal a strong long-term association among cryptocurrencies, with a decline in medium-term correlations. Bitcoin exhibits synchronization with major cryptocurrencies, excluding Tether, while BTC-ETH and BTC-BNB display a rapid, interconnected behavior alongside their fundamental links. Moreover, empirical evidence indicates Bitcoin’s heterogeneous nexus with other alternatives, showcasing greater sensitivity to positive extremes over negative ones.
Research limitations – The study’s scope is delimited by the selected time frame (July 2017 to May 2024) for data analysis, potentially limiting insights into longer-term trends. Additionally, the reliance on specific methodologies like wavelet analysis might introduce constraints in capturing the entirety of cryptocurrency dynamics, leaving room for alternative interpretations or unexplored aspects.
Practical implications – Results suggest that understanding the varying correlations among major cryptocurrencies during different market phases could aid investors and policymakers in devising more nuanced strategies. Recognizing the sensitivity of Bitcoin’s connections with alternatives to market trends could inform risk management approaches, particularly in navigating extreme market conditions.
Originality/Value – The originality of this study lies in its comprehensive examination of cryptocurrency dynamics across varying time scales, utilizing wavelet analysis and quantile regression techniques. The findings offer valuable insights into the complex interconnections among cryptocurrencies, especially in terms of their sensitivity to different market conditions, providing a nuanced perspective for investors, analysts, and policymakers navigating the crypto landscape.
Keyword : Bitcoin, Ethereum, cryptocurrencies, wavelets, co-movement
How to Cite
Gulseven, O., Almansour, B. Y., & Gaytan, J. C. T. (2024). Revisiting the dynamics of major cryptocurrencies. Business, Management and Economics Engineering, 22(2), 357–381. https://doi.org/10.3846/bmee.2024.20426
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Almansour, B.Y., Almansour, A.Y., & In’airat, M. (2020). The impact of exchange rates on bitcoin returns: Further evidence from a time series framework. International Journal of Scientific and Technology Research, 9(2), 4577–4581.
Apergis, N. (2023). Realized higher-order moments spillovers across cryptocurrencies. Journal of International Financial Markets, Institutions and Money, 85, Article 101763. https://doi.org/10.1016/j.intfin.2023.101763
Arif, M., Hasan, M., Alawi, S. M., & Naeem, M. A. (2021). COVID-19 and time-frequency connectedness between green and conventional financial markets. Global Finance Journal, 49, Article 100650. https://doi.org/10.1016/j.gfj.2021.100650
Bai, L., Wei, Y., Wei, G., Li, X., & Zhang, S. (2020). Infectious disease pandemic and permanent volatility of international stock markets: A long-term perspective. Finance Research Letters, 40, Article 101709. https://doi.org/10.1016/j.frl.2020.101709
Balli, F., de Bruin, A., Chowdhury, M. I. H., & Naeem, M. A. (2020). Connectedness of cryptocurrencies and prevailing uncertainties. Applied Economics Letters, 27(16), 1316–1322. https://doi.org/10.1080/13504851.2019.1678724
Bouri, E., Gupta, R., Tiwari, A. K., & Roubaud, D. (2017). Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Finance Research Letters, 23, 87–95. https://doi.org/10.1016/j.frl.2017.02.009
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Daubechies, I. (1988). Orthonormal bases of compactly supported wavelets. Communications on Pure and Applied Mathematics, 41(7), 909–996. https://doi.org/10.1002/cpa.3160410705
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Fang, L., Bouri, E., Gupta, R., & Rouband, D. (2019). Does global economic uncertainty matter for the volatility and hedging effectiveness of Bitcoin? International Review of Financial Analysis, 61, 29–36. https://doi.org/10.1016/j.irfa.2018.12.010
Fidrmuc, J., Kapounek, S., & Junge, F. (2020). Cryptocurrency market efficiency: Evidence from wavelet analysis. Finance a Uver: Czech Journal of Economics & Finance, 70(2), 121–144.
Fruehwirt, W., Hochfilzer, L., Weydemann, L., & Roberts, S. (2020). Cumulation, crash, coherency: A cryptocurrency bubble wavelet analysis. Finance Research Letters, 40, Article 101668. https://doi.org/10.1016/j.frl.2020.101668
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Gulseven, O., & Ekici, O. (2016). The Turkish appetite for gold: An Islamic explanation. Resources Policy, 48, 41–49. https://doi.org/10.1016/j.resourpol.2016.02.006
Huo, C., Hameed, J., Sharif, A., Albasher, G., Alamri, O., Alsultan, N., & Baig, N. (2022). Recent scenario and nexus globalization to CO2 emissions: Evidence from wavelet and Quantile Regression approach. Environmental Research, 212, Article 113067. https://doi.org/10.1016/j.envres.2022.113067
Hsu, S., Shwu, C., & Yoon, J. (2021). Risk spillovers between cryptocurrencies and traditional currencies and gold under different global economic conditions. North American Journal of Economics and Finance, 57, Article 101443. https://doi.org/10.1016/j.najef.2021.101443
Hung, N. T. (2021). Bitcoin and CEE stock markets: Fresh evidence from using the DECO-GARCH model and quantile on quantile regression. European Journal of Management and Business Economics, 30(2), 261–280. https://doi.org/10.1108/EJMBE-06-2020-0169
Hung, N. T. (2023). Green investment, financial development, digitalization and economic sustainability in Vietnam: Evidence from a quantile-on-quantile regression and wavelet coherence. Technological Forecasting & Social Change, 186, Article 122185. https://doi.org/10.1016/j.techfore.2022.122185
Juškaitė, L., & Gudelytė-Žilinskienė, L. (2022). Investigation of the feasibility of including different cryptocurrencies in the investment portfolio for its diversification. Business, Management and Economics Engineering, 20(1), 172–188. https://doi.org/10.3846/bmee.2022.16883
Kang, S. H., McIver, R. P., & Hernandez, J. A. (2019). Co-movements between Bitcoin and Gold: A wavelet coherence analysis. Physica A: Statistical Mechanics and its Applications, 536, Article 120888. https://doi.org/10.1016/j.physa.2019.04.124
Khalfaoui, R., Mefteh-Wali, S., Dogan, B., & Ghosh, S. (2023). Extreme spillover effect of COVID-19 pandemic-related news and cryptocurrencies on green bond markets: A quantile connectedness analysis. International Review of Financial Analysis, 86, Article 102496. https://doi.org/10.1016/j.irfa.2023.102496
Kumah, S. P., & Mensah, J. O. (2022). Are cryptocurrencies connected to gold? A wavelet-based quantile-in-quantile approach. International Journal of Finance and Economics, 27(3), 3640–3659. https://doi.org/10.1002/ijfe.2342
Kumah, S. P., & Odei-Mensah, J. (2022). Do cryptocurrencies and crude oil influence each other? Evidence from wavelet-based quantile-in-quantile approach. Cogent Economics and Finance, 10(1), Article 2082027. https://doi.org/10.1080/23322039.2022.2082027
Le, T. P. T. D., & Tran, H. L. M. (2021). The contagion effect from U.S. stock market to the Vietnamese and the Philippine stock markets: The evidence of DCC – GARCH model. Journal of Asian Finance, Economics and Business, 8(2), 759–770.
Li, R., Li, S., Yuan, D., & Zhu, H. (2021). Investor attention and cryptocurrency: Evidence from wavelet-based quantile Granger causality analysis. Research in International Business and Finance, 56, Article 101389. https://doi.org/10.1016/j.ribaf.2021.101389
López-Martín, C., Benito Muela, S., & Arguedas, R. (2021). Efficiency in cryptocurrency markets: New evidence. Eurasian Economic Review, 11(3), 403–431. https://doi.org/10.1007/s40822-021-00182-5
Luu, Q. T. & Luong, H. T. T. (2020). Herding behavior in emerging and frontier stock markets during pandemic influenza panics. The Journal of Asian Finance, Economics and Business, 7(9), 147–158. https://doi.org/10.13106/jafeb.2020.vol7.no9.147
Maitra, D., Rehman, M. U., & Dash, S. R. (2022). Do cryptocurrencies provide better hedging? Evidence from major equity markets during COVID-19 pandemic. North American Journal of Economics and Finance, 62, Article 101776. https://doi.org/10.1016/j.najef.2022.101776
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
Nurdany, A., Ibrahim, M. H., & Romadoni, M. F. (2021). The asymmetric volatility of the Islamic capital market during the COVID-19 pandemic. Journal of Islamic Monetary Economics and Finance, 7(1), 185–202. https://doi.org/10.21098/jimf.v7i0.1312
Omane-Adjepong, M., Alagidede, P., & Akosah, N. K. (2019). Wavelet time-scale persistence analysis of cryptocurrency market returns and volatility. Physica A: Statistical Mechanics and Its Applications, 514, 105–120. https://doi.org/10.1016/j.physa.2018.09.013
Sim, N., & Zhou, H. (2015). Oil prices, US stock returns, and the dependence between their quantiles. Journal of Banking & Finance, 55, 1–8. https://doi.org/10.1016/j.jbankfin.2015.01.013
Tapia, S., & Kristjanpoller, W. (2022). Framework based on multiplicative error and residual analysis to forecast bitcoin intraday-volatility. Physica A: Statistical Mechanics and Its Applications, 589, Article 126613. https://doi.org/10.1016/j.physa.2021.126613
Torrence, C., & Compo, G. (1998). A practical guide to wavelets analysis. Bulletin of the American Meteorological Society, 79, 61–78. https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
Torrence, C., & Webster, P. J. (1999). Interdecadal changes in the ENSO-monsoon system. Journal of Climate, 12(8), 2679–2690. https://doi.org/10.1175/1520-0442(1999)012<2679:ICITEM>2.0.CO;2
Trucíos, C., & Taylor, J. W. (2023). A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies. Journal of Forecasting, 42(4), 989–1007. https://doi.org/10.1002/for.2929
Umar, M., Su, C.-W., Rizvi, S. K. A., & Shao, X.-F. (2021). Bitcoin: A safe haven asset and a winner amid political and economic uncertainties in the US? Technological Forecasting and Social Change, 167, Article 120680. https://doi.org/10.1016/j.techfore.2021.120680
Umar, M., Shahzad, F., Ullah, I., & Fanghua, T. (2023). A comparative analysis of cryptocurrency returns and economic policy uncertainty pre- and post-COVID-19. Research in International Business and Finance, 65, Article 101965. https://doi.org/10.1016/j.ribaf.2023.101965
Uzonwanne, G. (2021). Volatility and return spillovers between stock markets and cryptocurrencies. The Quarterly Review of Economics and Finance, 82, 30–36. https://doi.org/10.1016/j.qref.2021.06.018
Xu, L., & Kinkyo, T. (2023). Hedging effectiveness of bitcoin and gold: Evidence from G7 stock markets. Journal of International Financial Markets, Institutions and Money, 85, Article 101764. https://doi.org/10.1016/j.intfin.2023.101764
Zhang, Y.-J., Bouri, E., Gupta, R., & Ma, S.-J. (2020). Risk spillover between Bitcoin and conventional financial markets: An expectile-based approach. The North American Journal of Economics and Finance, 55, Article 101296. https://doi.org/10.1016/j.najef.2020.101296
Wang, P., Zhang, H., Yang, C., & Guo, Y. (2021). Time and frequency dynamics of connectedness and hedging performance in global stock markets: Bitcoin versus conventional hedges. Research in International Business and Finance, 58, Article 101479. https://doi.org/10.1016/j.ribaf.2021.101479
Akyildirim, E., Aysan, A. F., Cepni, O., & Darendeli, S. P. C. (2021). Do investor sentiments drive cryptocurrency prices? Economics Letters, 206, Article 109980. https://doi.org/10.1016/j.econlet.2021.109980
Almansour, B.Y., Almansour, A.Y., & In’airat, M. (2020). The impact of exchange rates on bitcoin returns: Further evidence from a time series framework. International Journal of Scientific and Technology Research, 9(2), 4577–4581.
Apergis, N. (2023). Realized higher-order moments spillovers across cryptocurrencies. Journal of International Financial Markets, Institutions and Money, 85, Article 101763. https://doi.org/10.1016/j.intfin.2023.101763
Arif, M., Hasan, M., Alawi, S. M., & Naeem, M. A. (2021). COVID-19 and time-frequency connectedness between green and conventional financial markets. Global Finance Journal, 49, Article 100650. https://doi.org/10.1016/j.gfj.2021.100650
Bai, L., Wei, Y., Wei, G., Li, X., & Zhang, S. (2020). Infectious disease pandemic and permanent volatility of international stock markets: A long-term perspective. Finance Research Letters, 40, Article 101709. https://doi.org/10.1016/j.frl.2020.101709
Balli, F., de Bruin, A., Chowdhury, M. I. H., & Naeem, M. A. (2020). Connectedness of cryptocurrencies and prevailing uncertainties. Applied Economics Letters, 27(16), 1316–1322. https://doi.org/10.1080/13504851.2019.1678724
Bouri, E., Gupta, R., Tiwari, A. K., & Roubaud, D. (2017). Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Finance Research Letters, 23, 87–95. https://doi.org/10.1016/j.frl.2017.02.009
Claessens, S., & Kose, M. A. (2013). Financial crises: Explanations, types, and implications (Working Paper No. 13/28). International Monetary Fund.
CoinMarketCap. (2024). Cryptocurrency prices, charts, and market capitalizations. Retrieved May 19, 2024, from https://coinmarketcap.com/
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. https://doi.org/10.1016/j.irfa.2018.09.003
Cross, J. L., Hou, C., & Trinh, K. (2021). Returns, volatility and the cryptocurrency bubble of 2017–18. Economic Modelling, 104, Article 105643. https://doi.org/10.1016/j.econmod.2021.105643
Daubechies, I. (1988). Orthonormal bases of compactly supported wavelets. Communications on Pure and Applied Mathematics, 41(7), 909–996. https://doi.org/10.1002/cpa.3160410705
Demir, E., Bilgin, M. H., Karabulut, G., & Doker, A. C. (2020). The relationship between cryptocurrencies and COVID-19 pandemic. Eurasian Economic Review, 10(3), 349–360. https://doi.org/10.1007/s40822-020-00154-1
Diebold, F. X. & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006
Fang, L., Bouri, E., Gupta, R., & Rouband, D. (2019). Does global economic uncertainty matter for the volatility and hedging effectiveness of Bitcoin? International Review of Financial Analysis, 61, 29–36. https://doi.org/10.1016/j.irfa.2018.12.010
Fidrmuc, J., Kapounek, S., & Junge, F. (2020). Cryptocurrency market efficiency: Evidence from wavelet analysis. Finance a Uver: Czech Journal of Economics & Finance, 70(2), 121–144.
Fruehwirt, W., Hochfilzer, L., Weydemann, L., & Roberts, S. (2020). Cumulation, crash, coherency: A cryptocurrency bubble wavelet analysis. Finance Research Letters, 40, Article 101668. https://doi.org/10.1016/j.frl.2020.101668
Ge, Z. (2023). The asymmetric impact of oil price shocks on China stock market: Evidence from quantile-on-quantile regression. Quarterly Review of Economics and Finance, 89, 120 –125. https://doi.org/10.1016/j.qref.2023.03.009
Grinsted, A., Moore, J. C., & Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonline Processes Geophysics, 11(5/6), 561–566. https://doi.org/10.5194/npg-11-561-2004
Gulseven, O., & Ekici, O. (2016). The Turkish appetite for gold: An Islamic explanation. Resources Policy, 48, 41–49. https://doi.org/10.1016/j.resourpol.2016.02.006
Huo, C., Hameed, J., Sharif, A., Albasher, G., Alamri, O., Alsultan, N., & Baig, N. (2022). Recent scenario and nexus globalization to CO2 emissions: Evidence from wavelet and Quantile Regression approach. Environmental Research, 212, Article 113067. https://doi.org/10.1016/j.envres.2022.113067
Hsu, S., Shwu, C., & Yoon, J. (2021). Risk spillovers between cryptocurrencies and traditional currencies and gold under different global economic conditions. North American Journal of Economics and Finance, 57, Article 101443. https://doi.org/10.1016/j.najef.2021.101443
Hung, N. T. (2021). Bitcoin and CEE stock markets: Fresh evidence from using the DECO-GARCH model and quantile on quantile regression. European Journal of Management and Business Economics, 30(2), 261–280. https://doi.org/10.1108/EJMBE-06-2020-0169
Hung, N. T. (2023). Green investment, financial development, digitalization and economic sustainability in Vietnam: Evidence from a quantile-on-quantile regression and wavelet coherence. Technological Forecasting & Social Change, 186, Article 122185. https://doi.org/10.1016/j.techfore.2022.122185
Juškaitė, L., & Gudelytė-Žilinskienė, L. (2022). Investigation of the feasibility of including different cryptocurrencies in the investment portfolio for its diversification. Business, Management and Economics Engineering, 20(1), 172–188. https://doi.org/10.3846/bmee.2022.16883
Kang, S. H., McIver, R. P., & Hernandez, J. A. (2019). Co-movements between Bitcoin and Gold: A wavelet coherence analysis. Physica A: Statistical Mechanics and its Applications, 536, Article 120888. https://doi.org/10.1016/j.physa.2019.04.124
Khalfaoui, R., Mefteh-Wali, S., Dogan, B., & Ghosh, S. (2023). Extreme spillover effect of COVID-19 pandemic-related news and cryptocurrencies on green bond markets: A quantile connectedness analysis. International Review of Financial Analysis, 86, Article 102496. https://doi.org/10.1016/j.irfa.2023.102496
Kumah, S. P., & Mensah, J. O. (2022). Are cryptocurrencies connected to gold? A wavelet-based quantile-in-quantile approach. International Journal of Finance and Economics, 27(3), 3640–3659. https://doi.org/10.1002/ijfe.2342
Kumah, S. P., & Odei-Mensah, J. (2022). Do cryptocurrencies and crude oil influence each other? Evidence from wavelet-based quantile-in-quantile approach. Cogent Economics and Finance, 10(1), Article 2082027. https://doi.org/10.1080/23322039.2022.2082027
Le, T. P. T. D., & Tran, H. L. M. (2021). The contagion effect from U.S. stock market to the Vietnamese and the Philippine stock markets: The evidence of DCC – GARCH model. Journal of Asian Finance, Economics and Business, 8(2), 759–770.
Li, R., Li, S., Yuan, D., & Zhu, H. (2021). Investor attention and cryptocurrency: Evidence from wavelet-based quantile Granger causality analysis. Research in International Business and Finance, 56, Article 101389. https://doi.org/10.1016/j.ribaf.2021.101389
López-Martín, C., Benito Muela, S., & Arguedas, R. (2021). Efficiency in cryptocurrency markets: New evidence. Eurasian Economic Review, 11(3), 403–431. https://doi.org/10.1007/s40822-021-00182-5
Luu, Q. T. & Luong, H. T. T. (2020). Herding behavior in emerging and frontier stock markets during pandemic influenza panics. The Journal of Asian Finance, Economics and Business, 7(9), 147–158. https://doi.org/10.13106/jafeb.2020.vol7.no9.147
Maitra, D., Rehman, M. U., & Dash, S. R. (2022). Do cryptocurrencies provide better hedging? Evidence from major equity markets during COVID-19 pandemic. North American Journal of Economics and Finance, 62, Article 101776. https://doi.org/10.1016/j.najef.2022.101776
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
Nurdany, A., Ibrahim, M. H., & Romadoni, M. F. (2021). The asymmetric volatility of the Islamic capital market during the COVID-19 pandemic. Journal of Islamic Monetary Economics and Finance, 7(1), 185–202. https://doi.org/10.21098/jimf.v7i0.1312
Omane-Adjepong, M., Alagidede, P., & Akosah, N. K. (2019). Wavelet time-scale persistence analysis of cryptocurrency market returns and volatility. Physica A: Statistical Mechanics and Its Applications, 514, 105–120. https://doi.org/10.1016/j.physa.2018.09.013
Sim, N., & Zhou, H. (2015). Oil prices, US stock returns, and the dependence between their quantiles. Journal of Banking & Finance, 55, 1–8. https://doi.org/10.1016/j.jbankfin.2015.01.013
Tapia, S., & Kristjanpoller, W. (2022). Framework based on multiplicative error and residual analysis to forecast bitcoin intraday-volatility. Physica A: Statistical Mechanics and Its Applications, 589, Article 126613. https://doi.org/10.1016/j.physa.2021.126613
Torrence, C., & Compo, G. (1998). A practical guide to wavelets analysis. Bulletin of the American Meteorological Society, 79, 61–78. https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
Torrence, C., & Webster, P. J. (1999). Interdecadal changes in the ENSO-monsoon system. Journal of Climate, 12(8), 2679–2690. https://doi.org/10.1175/1520-0442(1999)012<2679:ICITEM>2.0.CO;2
Trucíos, C., & Taylor, J. W. (2023). A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies. Journal of Forecasting, 42(4), 989–1007. https://doi.org/10.1002/for.2929
Umar, M., Su, C.-W., Rizvi, S. K. A., & Shao, X.-F. (2021). Bitcoin: A safe haven asset and a winner amid political and economic uncertainties in the US? Technological Forecasting and Social Change, 167, Article 120680. https://doi.org/10.1016/j.techfore.2021.120680
Umar, M., Shahzad, F., Ullah, I., & Fanghua, T. (2023). A comparative analysis of cryptocurrency returns and economic policy uncertainty pre- and post-COVID-19. Research in International Business and Finance, 65, Article 101965. https://doi.org/10.1016/j.ribaf.2023.101965
Uzonwanne, G. (2021). Volatility and return spillovers between stock markets and cryptocurrencies. The Quarterly Review of Economics and Finance, 82, 30–36. https://doi.org/10.1016/j.qref.2021.06.018
Xu, L., & Kinkyo, T. (2023). Hedging effectiveness of bitcoin and gold: Evidence from G7 stock markets. Journal of International Financial Markets, Institutions and Money, 85, Article 101764. https://doi.org/10.1016/j.intfin.2023.101764
Zhang, Y.-J., Bouri, E., Gupta, R., & Ma, S.-J. (2020). Risk spillover between Bitcoin and conventional financial markets: An expectile-based approach. The North American Journal of Economics and Finance, 55, Article 101296. https://doi.org/10.1016/j.najef.2020.101296
Wang, P., Zhang, H., Yang, C., & Guo, Y. (2021). Time and frequency dynamics of connectedness and hedging performance in global stock markets: Bitcoin versus conventional hedges. Research in International Business and Finance, 58, Article 101479. https://doi.org/10.1016/j.ribaf.2021.101479