Weak efficiency of the cryptocurrency market: a market portfolio approach

References

• Bachelier, L. 1900. "Théorie de la spéculation". Annales scientifiques de l'École Normale Supérieure 3 (17): 21-86. doi:10.24033/asens.476.
   Google Scholar
• Baek, C., and M. Elbeck. 2015. “Bitcoins as an Investment or Speculative Vehicle? A First Look.” Applied Economics Letters 22 (1): 30–34. doi:10.1080/13504851.2014.916379.
   Web of Science ®Google Scholar
• Bariviera, A. F. 2017. “The Inefficiency of Bitcoin Revisited: A Dynamic Approach.” Economics Letters 161: 1–4. doi:10.1016/j.econlet.2017.09.013.
   Web of Science ®Google Scholar
• Bartels, R. 1982. “The Rank Version of Von Neumann’s Ratio Test for Randomness.” Journal of the American Statistical Association 77 (377): 40–46. doi:10.1080/01621459.1982.10477764.
   Web of Science ®Google Scholar
• BNC (BraveNewCoin). 2018. “Data from BraveNewCoin.” http://bravenewcoin.com/
   Google Scholar
• Brauneis, A., and R. Mestel. 2018. “Price Discovery of Cryptocurrencies: Bitcoin and Beyond.” Economics Letters 165: 58–61. doi:10.1016/j.econlet.2018.02.001.
   Web of Science ®Google Scholar
• Broock, W. A., J. A. Scheinkman, W. Davis Dechert, and B. LeBaron. 1996. “A Test for Independence Based on the Correlation Dimension.” Econometric Reviews 15 (3): 197–235. doi:10.1080/07474939608800353.
   Google Scholar
• Campbell, J. Y.,  A. W. Lo and  A. C. MacKinla 1997. The Econometrics of Financial Markets. Vol. 2. Princeton, NJ: princeton University press.
   Google Scholar
• Charles, A., O. Darné, and J. Fouilloux. 2011. “Testing the Martingale Difference Hypothesis in CO2 Emission Allowances.” Economic Modelling 28 (1–2): 27–35. doi:10.1016/j.econmod.2010.10.003.
   Web of Science ®Google Scholar
• Charles, A., O. Darné, and J. H. Kim. 2011. “Small Sample Properties of Alternative Tests for Martingale Difference Hypothesis.” Economics Letters 110 (2): 151–154. doi:10.1016/j.econlet.2010.11.018.
   Web of Science ®Google Scholar
• Chen, S., C. Chen, W. K. Härdle, T. M. Lee, and B. Ong. 2016. A First Econometric Analysis of the CRIX Family. SSRN working paper. Available at: http://dx.doi.org/10.2139/ssrn.2832099.
   Google Scholar
• Chow, K. V., and K. C. Denning. 1993. “A Simple Multiple Variance Ratio Test.” Journal of Econometrics 58 (3): 385–401. doi:10.1016/0304-4076(93)90051-6.
   Web of Science ®Google Scholar
• Ciaian, Pavel, Miroslava Rajcaniova 2017. “Virtual Relationships: Short-And Long-Run Evidence from BitCoin and Altcoin Markets.” Journal of International Financial Markets, Institutions and Money 52: 173–195.
   Web of Science ®Google Scholar
• CMC. 2018. “Data from Coinmarketcap.” http://coinmarketcap.com/
   Google Scholar
• Cowles 3rd, A. 1933. “Can Stock Market Forecasters Forecast?” Econometrica: Journal of the Econometric Society 1: 309–324. doi:10.2307/1907042.
   Google Scholar
• Escanciano, J. C., and I. N. Lobato. 2009a. “An Automatic Portmanteau Test for Serial Correlation.” Journal of Econometrics 151 (2): 140–149. doi:10.1016/j.jeconom.2009.03.001.
   Web of Science ®Google Scholar
• Escanciano, J. C., and I. N. Lobato. 2009b. “Testing the Martingale Hypothesis.” In Palgrave Handbook of Econometrics, 972–1003. Palgrave Macmillan, London.
   Google Scholar
• Escanciano, J. C., and C. Velasco. 2006. “Generalized Spectral Tests for the Martingale Difference Hypothesis.” Journal of Econometrics 134 (1): 151–185. doi:10.1016/j.jeconom.2005.06.019.
   Web of Science ®Google Scholar
• Fama, E. F. 1965. “The Behavior of Stock-Market Prices.” The Journal of Business 38 (1): 34–105. doi:10.1086/294743.
   Web of Science ®Google Scholar
• Gandal, N., and H. Halaburda. 2016. “Can We Predict the Winner in a Market with Network Effects? Competition in Cryptocurrency Market.” Games 7 (3): 16. doi:10.3390/g7030016.
   Google Scholar
• Jiang, Y., H. Nie, and W. Ruan. 2017. “Time-Varying Long-Term Memory in Bitcoin Market.” Finance Research Letters 25: 280–284.
   Web of Science ®Google Scholar
• Katsiampa, P. 2017. “Volatility Estimation for Bitcoin: A Comparison of GARCH Models.” Economics Letters 158: 3–6. doi:10.1016/j.econlet.2017.06.023.
   Web of Science ®Google Scholar
• Kim, J. H. 2009. “Automatic Variance Ratio Test under Conditional Heteroskedasticity.” Finance Research Letters 6 (3): 179–185. doi:10.1016/j.frl.2009.04.003.
   Web of Science ®Google Scholar
• LeRoy, S. F. 1973. “Risk Aversion and the Martingale Property of Stock Prices.” International Economic Review 14: 436–446. doi:10.2307/2525932.
   Google Scholar
• LeRoy, S. F. 1989. “Efficient Capital Markets and Martingales.” Journal of Economic Literature 27 (4): 1583–1621.
   Web of Science ®Google Scholar
• Lim, K.-P., and R. Brooks. 2011. “The Evolution of Stock Market Efficiency over Time: A Survey of the Empirical Literature.” Journal of Economic Surveys 25 (1): 69–108. doi:10.1111/j.1467-6419.2009.00611.x.
   Web of Science ®Google Scholar
• Ljung, G. M., and G. E. P. Box. 1978. “On a Measure of Lack of Fit in Time Series Models.” Biometrika 65 (2): 297–303. doi:10.1093/biomet/65.2.297.
   Web of Science ®Google Scholar
• Lo, A. W., and A. Craig MacKinlay. 1988. “Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test.” The Review of Financial Studies 1 (1): 41–66. doi:10.1093/rfs/1.1.41.
   Web of Science ®Google Scholar
• Lucas Jr, R. E. 1978. “Asset Prices in an Exchange Economy.” Econometrica: Journal of the Econometric Society 46: 1429–1445. doi:10.2307/1913837.
   Web of Science ®Google Scholar
• Malkiel, B. G., and K. McCue. 1985. A Random Walk down Wall Street. New York: Norton.
   Google Scholar
• Mills, T., and K. Patterson. 2009. Palgrave Handbook of Econometrics: Volume 2: Applied Econometrics. Vol. 2. Palgrave Macmillan.
   Google Scholar
• Nadarajah, S., and J. Chu. 2017. “On the Inefficiency of Bitcoin.” Economics Letters 150: 6–9. doi:10.1016/j.econlet.2016.10.033.
   Web of Science ®Google Scholar
• Osterrieder, J., M. Strika, and J. Lorenz. 2017. “Bitcoin and Cryptocurrencies—Not for the Faint-Hearted.” International Finance and Banking 4 (1): 56. doi:10.5296/ifb.v4i1.
   Google Scholar
• Phillip, A., J. Chan, and S. Peiris. 2017. “A New Look at Cryptocurrencies.” Economics Letters 163: 6–9.
   Web of Science ®Google Scholar
• Tiwari, A. K., R. K. Jana, D. Das, and D. Roubaud. 2017. “Informational Efficiency of Bitcoin—An Extension.” Economics Letters 163: 106–109.
   Web of Science ®Google Scholar
• Trimborn, S., and W. K. Härdle. 2016. CRIX an Index for Blockchain Based Currencies. SFB 649 Discussion Paper 2016-021, Economic Risk, Berlin. Available at: http://dx.doi.org/10.2139/ssrn.2800928.
   Google Scholar
• Urquhart, A. 2016. “The Inefficiency of Bitcoin.” Economics Letters 148: 80–82. doi:10.1016/j.econlet.2016.09.019.
   Web of Science ®Google Scholar
• Vidal-Tomás, D., and A. Ibañez. 2018. “Semi-Strong Efficiency of Bitcoin.” Finance Research Letters 27: 259–265. doi:10.1016/j.frl.2018.03.013.
   Web of Science ®Google Scholar
• Wald, A., and J. Wolfowitz. 1940. “On a Test whether Two Samples are from the Same Population.” The Annals of Mathematical Statistics 11 (2): 147–162. doi:10.1214/aoms/1177731909.
   Google Scholar
• Wei, W. C. 2018. “Liquidity and Market Efficiency in Cryptocurrencies.” Economics Letters 168: 21–24. doi:10.1016/j.econlet.2018.04.003.
   Web of Science ®Google Scholar
• Weron, R. 2002. “Estimating Long-Range Dependence: Finite Sample Properties and Confidence Intervals.” Physica A: Statistical Mechanics and Its Applications 312 (1–2): 285–299. doi:10.1016/S0378-4371(02)00961-5.
   Web of Science ®Google Scholar