PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2023 | 7 | nr 1 | 31--47
Tytuł artykułu

The weak-form efficiency of cryptocurrencies

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This study aimed to examine the weak-form efficiency of some of the most capitalised cryptocurrencies. The sample consisted of 24 cryptocurrencies selected out of 30 cryptocurrencies with the highest market capitalisation as of October 19, 2022. Stablecoins were not considered. The study covered the period from January 1, 2018 to August 31, 2022. The results of robust martingale difference hypothesis tests suggest that the examined cryptocurrencies were efficient most of the time. However, their efficiency turned out to be time-varying, which validates the adaptive market hypothesis. No evidence was found for the impact of the coronavirus outbreak and the Russian invasion of Ukraine on the weak-form efficiency of the examined cryptocurrencies. The differences in efficiency between the most efficient cryptocurrencies and the least efficient ones were noticeable, but not large. The results also allowed to observe some slight differences in efficiency between the cryptocurrencies with the largest market cap and cryptocurrencies with the lowest market cap. However, the differences between the two groups were too small to draw any far-reaching conclusions about a positive relationship between the market cap and efficiency. The obtained results also did not allow us to detect any trends in efficiency.(original abstract)
Rocznik
Tom
7
Numer
Strony
31--47
Opis fizyczny
Twórcy
  • University of Warsaw
Bibliografia
  • Alvarez-Ramirez, J., & Rodriguez, E. (2021). A singular value decomposition approach for testing the efficiency of Bitcoin and Ethereum markets. Economics Letters, 206, 1-5.
  • Apopo, N., & Phiri, A. (2021). On the (in)efficiency of cryptocurrencies: have they taken daily or weekly random walks? Heliyon, 7(4), 1-10.
  • Arouxet, M. B., Bariviera, A. F., Pastor, V. E., & Vampa, V. (2022). COVID-19 impact on cryptocurrencies: Evidence from a wavelet-based Hurst exponent. Physica A, 596, 1-12.
  • Assaf, A., Bhandari, A., Charif, H., & Demir, E. (2022a). Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19. International Review of Financial Analysis, 82, 1-17.
  • Assaf, A., Mokni, K., Yousaf, I., & Bhandari, A. (2022b). Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19. Research in International Business and Finance, 64, 1-19.
  • Aslam, F., Slim, S., Osman, M., & Tabche, I. (2022). The footprints of Russia-Ukraine war on the intraday (in)efficiency of energy markets: a multifractal analysis. Journal of Risk Finance, 24(1), 89-104.
  • Bundi, N., & Wildi, M. (2019). Bitcoin and market (in)efficiency: a systematic time series approach. Digital Finance, 1, 47-65.
  • Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The Econometrics of Financial Markets. Princeton. Princeton University Press.
  • Charles, A., Darné, O., & Kim, J. H. (2011). Small sample properties of alternative tests for martingale difference hypothesis. Economics Letters, 110(2), 151-154.
  • Chu, J., Zhang, Y., & Chan, S. (2019). The adaptive market hypothesis in the high frequency cryptocurrency market. International Review of Financial Analysis, 64(C), 221-231.
  • Escanciano, J. C., & Lobato, I. N. (2009). An automatic Portmanteau test for serial correlation. Journal of Econometrics, 151(2), 140-149.
  • Fama, E. F. (1965). The behaviour of stock market prices. Journal of Business, 38(1), 34-105.
  • Fama, E. F. (1970). Efficient capital markets: a review of theory and empirical work. Journal of Finance, 25(2), 383-417.
  • Gaio, L. E., Stefanelli, N. O., Pimenta Júnior, T., Bonacim, C. A. G., & Gatsios, R. C. (2022). The impact of the Russia-Ukraine conflict on market efficiency: Evidence for the developed stock market. Finance Research Letters, 50, 1-7.
  • Hawaldar, I. T., Mathukutti, R., & Dsouza, L. J. (2019). Testing the weak form of efficiency of cryptocurrencies: A case study of Bitcoin and Litecoin. International Journal of Scientific & Technology Research, 8(9), 2301-2305.
  • 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(C), 138-145.
  • 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, 1-10.
  • Khuntia, S., & Pattanayak, J. K. (2018). Adaptive market hypothesis and evolving predictability of Bitcoin. Economics Letters, 167, 26-28.
  • Khursheed, A., Naeem, M., Ahmed, S., & Mustafa, F. (2020). Adaptive market hypothesis: An empirical analysis of time-varying market efficiency of cryptocurrencies. Cogent Economics and Finance, 8(1), 1-15.
  • Kim, J. H. (2009). Automatic variance ratio test under conditional heteroskedasticity. Finance Research Letters, 6(3), 179-185.
  • Linton, O. (2019). Financial econometrics. Models and methods. Cambridge University Press.
  • Lo, A. W. (2004). The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. Journal of Portfolio Management, 30(5), 15-29.
  • Lo, A. W. (2005). Reconciling efficient markets with behavioral finance: The adaptive markets hypothesis. Journal of Investment Consulting, 7(2), 21-44.
  • López Martín, C., Muela, S. B., & Arguedas, R. (2021). Efficiency in cryptocurrency markets: New evidence. Eurasian Economic Review, 11(3), 403-431.
  • Mandaci, P. E., & Cagli, E. C. (2022). Herding intensity and volatility in cryptocurrency markets during the COVID-19. Finance Research Letters, 46, 1-7.
  • Mensi, W., Tiwari, A. K., & Al-Yahyaee, K. H. (2019). An analysis of the weak form efficiency, multifractality and long memory of global, regional and European stock markets. The Quarterly Review of Economics and Finance, 72, 168-177.
  • Nadarajah, S., & Chu, J. (2017). On the inefficiency of Bitcoin. Economics Letters, 150(C), 6-9.
  • Naeem, M. A., Bouri, E., Peng, Z., Shahzad, S. J. H., & Vo, X. V. (2021). Asymmetric efficiency of cryptocurrencies during COVID19. Physica A, 565, 1-12.
  • Noda, A. (2021). On the evolution of cryptocurrency market efficiency. Applied Economic Letters, 28(6), 433-439.
  • Palamalai, S., Kumar, K. K., & Maity, B. (2021). Testing the random walk hypothesis for leading cryptocurrencies. Borsa Istanbul Review, 21(3), 256-268.
  • Samuelson, P. A. (1965). Proof that properly anticipated prices fluctuate randomly. Industrial Management Review, 6, 41-49.
  • Sensoy, A. (2019). The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies. Finance Research Letters, 28(C), 68-73.
  • Tran, V. L., & Leirvik, T. (2019). A simple but powerful measure of market efficiency. Finance Research Letters, 29(C), 141-151.
  • Tran, V. L., & Leirvik, T. (2020). Efficiency in the markets of crypto-currencies. Finance Research Letters, 35(C).
  • Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148(C), 80-82. https://doi.org/10.1016/j.econlet.2016.09.019
  • Usman, N., & Nduka, K. N. (2022). Announcement effect of COVID-19 on cryptocurrencies. Asian Economics Letters, 3(3).
  • Yonghong, J., He, N., & Weihua, R. (2018). Time-varying long-term memory in Bitcoin market. Finance Research Letters, 25(C), 280-284.
  • Zhang, W., Wang, P., Li, X., & Shen, D. (2018). The inefficiency of cryptocurrency and its cross-correlation with Dow Jones Industrial Average. Physica A: Statistical Mechanics and Its Applications, 510, 658-670.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171677247

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.