IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v596y2022ics0378437122001765.html
   My bibliography  Save this article

Covid-19 impact on cryptocurrencies: Evidence from a wavelet-based Hurst exponent

Author

Listed:
  • Arouxet, M. Belén
  • Bariviera, Aurelio F.
  • Pastor, Verónica E.
  • Vampa, Victoria

Abstract

Cryptocurrency history begins in 2008 as a means of payment proposal. However, cryptocurrencies evolved into a complex ecosystem of high yield speculative assets. Contrary to traditional financial instruments, they are not (mostly) traded in organized, law-abiding venues, but on online platforms, where anonymity reigns. This paper examines the long term memory in return and volatility, using high frequency time series of seven important coins. Our study covers the pre-Covid-19 and the subsequent pandemic period. We use a recently developed method, based on the wavelet transform, which provides more robust estimators of the Hurst exponent. We detect that, during the peak of Covid-19 pandemic (around March 2020), the long memory of returns was only mildly affected. However, volatility suffered a temporary impact in its long range correlation structure. Our results could be of interest for both academics and practitioners.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:596:y:2022:i:c:s0378437122001765
    DOI: 10.1016/j.physa.2022.127170
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122001765
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.127170?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Sergio R. S. Souza & Benjamin M. Tabak & Daniel O. Cajueiro, 2008. "Long-Range Dependence In Exchange Rates: The Case Of The European Monetary System," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 11(02), pages 199-223.
    2. Cotter, John, 2004. "Absolute Return Volatility," MPRA Paper 3529, University Library of Munich, Germany, revised 2005.
    3. Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Pawe{l} O'swik{e}cimka & Tomasz Stanisz & Marcin Wk{a}torek, 2020. "Complexity in economic and social systems: cryptocurrency market at around COVID-19," Papers 2009.10030, arXiv.org.
    4. Garcin, Matthieu, 2017. "Estimation of time-dependent Hurst exponents with variational smoothing and application to forecasting foreign exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 462-479.
    5. Mnif, Emna & Jarboui, Anis & Mouakhar, Khaireddine, 2020. "How the cryptocurrency market has performed during COVID 19? A multifractal analysis," Finance Research Letters, Elsevier, vol. 36(C).
    6. Serinaldi, Francesco, 2010. "Use and misuse of some Hurst parameter estimators applied to stationary and non-stationary financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2770-2781.
    7. Epaminondas Panas, 2001. "Estimating fractal dimension using stable distributions and exploring long memory through ARFIMA models in Athens Stock Exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 11(4), pages 395-402.
    8. Barkoulas, John T. & Baum, Christopher F., 1996. "Long-term dependence in stock returns," Economics Letters, Elsevier, vol. 53(3), pages 253-259, December.
    9. Bouri, Elie & Azzi, Georges & Dyhrberg, Anne Haubo, 2017. "On the return-volatility relationship in the Bitcoin market around the price crash of 2013," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 11, pages 1-16.
    10. Bariviera, Aurelio F., 2021. "One model is not enough: Heterogeneity in cryptocurrencies’ multifractal profiles," Finance Research Letters, Elsevier, vol. 39(C).
    11. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    12. Bariviera, Aurelio F., 2017. "The inefficiency of Bitcoin revisited: A dynamic approach," Economics Letters, Elsevier, vol. 161(C), pages 1-4.
    13. C. Alexander & M. Dakos, 2020. "A critical investigation of cryptocurrency data and analysis," Quantitative Finance, Taylor & Francis Journals, vol. 20(2), pages 173-188, February.
    14. Cajueiro, Daniel O. & Tabak, Benjamin M., 2010. "Fluctuation dynamics in US interest rates and the role of monetary policy," Finance Research Letters, Elsevier, vol. 7(3), pages 163-169, September.
    15. Stanis{l}aw Dro.zd.z & Robert Gk{e}barowski & Ludovico Minati & Pawe{l} O'swik{e}cimka & Marcin Wk{a}torek, 2018. "Bitcoin market route to maturity? Evidence from return fluctuations, temporal correlations and multiscaling effects," Papers 1804.05916, arXiv.org, revised Jul 2018.
    16. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    17. Yang, Yan-Hong & Shao, Ying-Hui & Shao, Hao-Lin & Stanley, H. Eugene, 2019. "Revisiting the weak-form efficiency of the EUR/CHF exchange rate market: Evidence from episodes of different Swiss franc regimes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 734-746.
    18. Bariviera, A.F. & Guercio, M. Belén & Martinez, Lisana B., 2012. "A comparative analysis of the informational efficiency of the fixed income market in seven European countries," Economics Letters, Elsevier, vol. 116(3), pages 426-428.
    19. Al-Yahyaee, Khamis Hamed & Mensi, Walid & Ko, Hee-Un & Yoon, Seong-Min & Kang, Sang Hoon, 2020. "Why cryptocurrency markets are inefficient: The impact of liquidity and volatility," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    20. A. Ronald Gallant & Chien-Te Hsu & George Tauchen, 1999. "Using Daily Range Data To Calibrate Volatility Diffusions And Extract The Forward Integrated Variance," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 617-631, November.
    21. Aslan, Aylin & Sensoy, Ahmet, 2020. "Intraday efficiency-frequency nexus in the cryptocurrency markets," Finance Research Letters, Elsevier, vol. 35(C).
    22. Nadarajah, Saralees & Chu, Jeffrey, 2017. "On the inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 150(C), pages 6-9.
    23. Katsiampa, Paraskevi, 2019. "Volatility co-movement between Bitcoin and Ether," Finance Research Letters, Elsevier, vol. 30(C), pages 221-227.
    24. Goodell, John W. & Goutte, Stephane, 2021. "Co-movement of COVID-19 and Bitcoin: Evidence from wavelet coherence analysis," Finance Research Letters, Elsevier, vol. 38(C).
    25. Matsushita, Raul & Gleria, Iram & Figueiredo, Annibal & Da Silva, Sergio, 2007. "Are Pound and Euro the Same Currency? - Updated," MPRA Paper 1981, University Library of Munich, Germany.
    26. Aurelio Fernández Bariviera & M. Belén Guercio & Lisana B. Martinez, 2014. "Informational Efficiency in Distressed Markets: The Case of European Corporate Bonds," The Economic and Social Review, Economic and Social Studies, vol. 45(3), pages 349-369.
    27. Aurelio F. Bariviera & Luciano Zunino & Osvaldo A. Rosso, 2018. "An analysis of high-frequency cryptocurrencies prices dynamics using permutation-information-theory quantifiers," Papers 1808.01926, arXiv.org.
    28. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    29. Sensoy, A., 2013. "Effects of monetary policy on the long memory in interest rates: Evidence from an emerging market," Chaos, Solitons & Fractals, Elsevier, vol. 57(C), pages 85-88.
    30. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range‐Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, June.
    31. Carbone, A. & Castelli, G. & Stanley, H.E., 2004. "Time-dependent Hurst exponent in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 267-271.
    32. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    33. Azimli, Asil, 2020. "The impact of COVID-19 on the degree of dependence and structure of risk-return relationship: A quantile regression approach," Finance Research Letters, Elsevier, vol. 36(C).
    34. Cajueiro, Daniel O. & Tabak, Benjamin M., 2005. "Possible causes of long-range dependence in the Brazilian stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 345(3), pages 635-645.
    35. Benoit B. Mandelbrot, 1972. "Statistical Methodology for Nonperiodic Cycles: From the Covariance To R/S Analysis," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 1, number 3, pages 259-290, National Bureau of Economic Research, Inc.
    36. Corbet, Shaen & Hou, Yang (Greg) & Hu, Yang & Larkin, Charles & Oxley, Les, 2020. "Any port in a storm: Cryptocurrency safe-havens during the COVID-19 pandemic," Economics Letters, Elsevier, vol. 194(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. James, Nick & Menzies, Max, 2023. "Collective infectivity of the pandemic over time and association with vaccine coverage and economic development," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Bejaoui, Azza & Frikha, Wajdi & Jeribi, Ahmed & Bariviera, Aurelio F., 2023. "Connectedness between emerging stock markets, gold, cryptocurrencies, DeFi and NFT: Some new evidence from wavelet analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 619(C).
    3. Balcilar, Mehmet & Ozdemir, Huseyin & Agan, Busra, 2022. "Effects of COVID-19 on cryptocurrency and emerging market connectedness: Empirical evidence from quantile, frequency, and lasso networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    4. Bariviera, Aurelio F. & Fabregat-Aibar, Laura & Sorrosal-Forradellas, Maria-Teresa, 2023. "Disentangling the impact of economic and health crises on financial markets," Research in International Business and Finance, Elsevier, vol. 65(C).
    5. Bejaoui, Azza & Mgadmi, Nidhal & Moussa, Wajdi, 2022. "On the relationship between Bitcoin and other assets during the outbreak of coronavirus: Evidence from fractional cointegration analysis," Resources Policy, Elsevier, vol. 77(C).
    6. Marcin Wk{a}torek & Jaros{l}aw Kwapie'n & Stanis{l}aw Dro.zd.z, 2023. "Cryptocurrencies Are Becoming Part of the World Global Financial Market," Papers 2303.00495, arXiv.org.
    7. Espinosa-Paredes, G. & Rodriguez, E. & Alvarez-Ramirez, J., 2022. "A singular value decomposition entropy approach to assess the impact of Covid-19 on the informational efficiency of the WTI crude oil market," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bariviera, Aurelio F., 2017. "The inefficiency of Bitcoin revisited: A dynamic approach," Economics Letters, Elsevier, vol. 161(C), pages 1-4.
    2. Lisana B. Martinez & M. Belén Guercio & Aurelio Fernandez Bariviera & Antonio Terceño, 2018. "The impact of the financial crisis on the long-range memory of European corporate bond and stock markets," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 45(1), pages 1-15, February.
    3. Bariviera, Aurelio F. & Basgall, María José & Hasperué, Waldo & Naiouf, Marcelo, 2017. "Some stylized facts of the Bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 82-90.
    4. Bariviera, Aurelio F., 2021. "One model is not enough: Heterogeneity in cryptocurrencies’ multifractal profiles," Finance Research Letters, Elsevier, vol. 39(C).
    5. Jaros{l}aw Kwapie'n & Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z, 2021. "Cryptocurrency Market Consolidation in 2020--2021," Papers 2112.06552, arXiv.org.
    6. Yi, Eojin & Ahn, Kwangwon & Choi, M.Y., 2022. "Cryptocurrency: Not far from equilibrium," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    7. Assaf, Ata & Mokni, Khaled & Yousaf, Imran & Bhandari, Avishek, 2023. "Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19," Research in International Business and Finance, Elsevier, vol. 64(C).
    8. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    9. 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.
    10. Manahov, Viktor & Urquhart, Andrew, 2021. "The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets," International Review of Financial Analysis, Elsevier, vol. 73(C).
    11. Merediz-Solà, Ignasi & Bariviera, Aurelio F., 2019. "A bibliometric analysis of bitcoin scientific production," Research in International Business and Finance, Elsevier, vol. 50(C), pages 294-305.
    12. 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).
    13. Aslanidis, Nektarios & Bariviera, Aurelio F. & Martínez-Ibañez, Oscar, 2019. "An analysis of cryptocurrencies conditional cross correlations," Finance Research Letters, Elsevier, vol. 31(C), pages 130-137.
    14. Assaf, Ata & Bhandari, Avishek & Charif, Husni & Demir, Ender, 2022. "Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19," International Review of Financial Analysis, Elsevier, vol. 82(C).
    15. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.
    16. Natália Costa & César Silva & Paulo Ferreira, 2019. "Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies," IJFS, MDPI, vol. 7(3), pages 1-12, September.
    17. Bariviera, Aurelio Fernández, 2011. "The influence of liquidity on informational efficiency: The case of the Thai Stock Market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4426-4432.
    18. Cristiana Vaz & Rui Pascoal & Helder Sebastião, 2021. "Price Appreciation and Roughness Duality in Bitcoin: A Multifractal Analysis," Mathematics, MDPI, vol. 9(17), pages 1-18, August.
    19. Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis Alberiko & Madigu, Godfrey & Romero-Rojo, Fatima, 2020. "Volatility persistence in cryptocurrency markets under structural breaks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 680-691.
    20. Aslan, Aylin & Sensoy, Ahmet, 2020. "Intraday efficiency-frequency nexus in the cryptocurrency markets," Finance Research Letters, Elsevier, vol. 35(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:596:y:2022:i:c:s0378437122001765. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.