Apopo, Natalay and Phiri, Andrew (2019): On the (in)efficiency of cryptocurrencies: Have they taken daily or weekly random walks?
PDF
MPRA_paper_94712.pdf Download (9MB) |
Abstract
The legitimacy of virtual currencies as an alternative form of monetary exchange has been the centre of an ongoing heated debated since the catastrophic global financial meltdown of 2007-2008. We contribute to the relative fresh body of empirical research on the informational market efficiency of cryptomarkets by investigating the weak-form efficiency of the top-five cryptocurrencies. In differing from previous studies, we implement random walk testing procedures which are robust to asymmetries and unobserved smooth structural breaks. Moreover, our study employs two frequencies of cryptocurrency returns, one corresponding to daily returns and the other to weekly returns. Our findings validate the random walk hypothesis for daily series hence validating the weak-form efficiency for daily returns. On the other hand, weekly returns are observed to be stationary processes which is evidence against weak-form efficiency for weekly returns. Overall, our study has important implications for market participants within cryptocurrency markets.
Item Type: | MPRA Paper |
---|---|
Original Title: | On the (in)efficiency of cryptocurrencies: Have they taken daily or weekly random walks? |
Language: | English |
Keywords: | Efficient Market Hypothesis (EMH); Cryptocurrencies; Random Walk Model (RWM); Flexible Fourier Form (FFF) unit root tests; Smooth structural breaks. |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E42 - Monetary Systems ; Standards ; Regimes ; Government and the Monetary System ; Payment Systems G - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency ; Event Studies ; Insider Trading |
Item ID: | 94712 |
Depositing User: | Dr. Andrew Phiri |
Date Deposited: | 27 Jun 2019 09:37 |
Last Modified: | 28 Sep 2019 07:29 |
References: | Aggarwal D. (2019), “Do Bitcoins follow a random walk model?”, Research in Economics, 73(1), 15-22. Ardia D., Bluteau K. and Ruede M. (2018), “Regime changes in Bitcoin GARCH volatility dynamics”, Finance Research Letters, (forthcoming). Bariviera A., Basgall M., Hasperue W. and Naiouf M. (2017), “Some stylized facts of the Bitcoin market”, Physica A: Statistical Mechanics and its Applications”, 484(15), 82-90. Becker R., Enders W. and Lee J. (2006), “A stationary test with an unknown number of smooth breaks”, Journal of Time Series Analysis, 27(3), 381-409. Bjerg O. (2015), “How is Bitcoin money?”, Theory, Culture and Society, 33(1), 53-72. Bohme R., Christin N., Edelman B. and Moore T. (2015), “Bitcoin: Economies, technology, and governance”, Journal of Economic Perspectives, 29(2), 213-238. Bouoiyour J. and Selmi R. (2016), “Bitcoin: A beginning of a new phase?”, Economics Bulletin, 36(3), 1430-1440. Bouri E., Gil-Alana L., Gupta R. and Roubaud D. (2019), “Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks”, International Journal of Finance and Economics, 24(1), 412-426. Breitung J. and Das S. (2005), “Panel unit root tests under cross-sectional dependence”, Statistica Neerlandica, 59(4), 414-433. Caporale G. and Plastun A. (2018), “The day of the week effect in the cryptocurrency market”, Finance Research Letters (forthcoming). Caporale G., Gil-Alana L. and Plastun A. (2018), “Persistence in the cryptocurrency market”, Research in International Business and Finance, 46, 141-148. Caporale G. and Zekokh T. (2019), “The day of the week effect in the cryptocurrency market”, Research in International Business and Finance, 48, 143-155. Chang Y. (2002), “Nonlinear IV unit root tests in panels with cross-sectional dependency”, Journal of Econometrics, 110(2), 261-292. Chu J., Chan S., Nadarajah S. and Osterrieder J. (2017), “GARCH modelling of cryptocurrencies”, Journal of Risk and Financial Management, 10(4), 1-15. Costantini M. and Lupi C. (2013), “A simple panel-CADF test for unit roots”, Oxford Bulletin of Economics and Statistics, 75(2), 276-296. de la Horra L., de la Fuente G. and Perote J. (2019), “The drivers of Bitcoin demand: A short and long-run analysis”, International Review of Financial Analysis, 62, 21-34. Dyhrberg A. (2016), “Bitcoin, gold and the dollar – A GARCH volatility analysis”, Finance Research Letters, 16, 85-92. Elliot, Rothenberg and Stock (1996), “Efficient test for an autoregressive unit root”, Econometrica, 64(4), 813-836. Enders W. and Lee J. (2012), “A unit root test using Fourier series to approximate smooth breaks”, Oxford Bulletin of Economics and Statistics, 74(4), 574-599. Fama E. (1965), “The behaviour of stock market prices”, The Journal of Business, 38(1), 34-105. Hu Y., Valera H. and Oxley L. (2019), “Market efficiency of the top market-cap cryptocurrencies: Further evidence from a panel framework”, Finance Research Letters, 31, 138-145. Huang R. and Hoje J. (1995), “Data frequency and the number of factors in stock returns”, Journal of Banking and Finance, 19(6), 987-1003. Kapetanois G., Shin Y. and Snell A. (2003), “Testing for a unit root in the nonlinear STAR framework”, Journal of Econometrics, 112(2), 359-379. Katsiampa P. (2017), “Volatility estimation for Bitcoin: A comparison of GARCH models”, Economic Letters, 158(C) 3-6, Kristoufek I. (2015), “What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis”, PLOS ONE, 10(4), 1-5. Kubat M. (2015), “Virtual currency Bitcoin in the scope of money definition and store of value”, Procedia Economics and Finance, 30, 409-416. Kurihara Y. and Fukushima A. (2017), “The market efficiency of Bitcoin: A weekly anomaly perspective”, Journal of Applied Finance and Banking, 7(3-4), 57-64. Latif S., Mohd M, Amin M. and Mohamad A. (2017), “Testing the weak-form of efficient market in cryptocurrency”, Journal of Engineering and Applied Sciences, 12(9), 2285-2288. Malkiel B. and Fama E. (1970), “Efficient capital markets: A review of theory and empirical work”, The Journal of finance, 25(2), 383-417. Mandelbrot B (1966), “Forecasts of future prices, unbiased markets, and the ‘martingale models”, The Journal of Business, 39(1), 242-255. Martikainen T., Perttunen J., Yli-Olli P. and Gunasekaran A. (1994), “The impact of return interval on common factors in stock returns: Evidence from thin security markets”, Journal of Banking and Finance, 18(4), 659-672. Mensi W., Al-Yahyaee K., Kang S. (2018), “Structural breaks and double long memory of cryptocurrency prices: A comparative analysis from Bitcoin and Ethereum”, Finance Research Letters, (forthcoming). Moon H. and Perron B. (2004), “Testing for a unit root in panels with dynamic factors”, Journal of Econometrics, 122(1), 81-126. Nadarajah S. and Chu J. (2017), “On the inefficiency of Bitcoin”, Economic Letters, 150, 6-9. Ng S. and Perron P. (2001), “Lag selection and the construction of unit root tests with good size and power”, Econometrica, 69(6), 1519-1554. Pascalau R. (2010), “Unit root tests with smooth breaks: An application to the Nelson-Plosser data set”, Applied Economic Letters, 17(6), 565-570. Peng Y., Albuquerque P., de Sa J., Padula A. and Montenegro M. (2018), “The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with support vector regressions”, Expert Systems With Applications, 17, 177-192. Perron P. (1989) , “The Great Crash, the oil price shock, and the unit root hypothesis”, Econometrica, 57(6), 1361-1401. Rodrigues P. and Taylor R. (2012), “The Flexible Fourier Form and local generalized least squares de-trended unit root tests”, Oxford Bulletin of Economics and Statistics, 74(5), 736-759. Samuelson P. (1965), “Proof that properly anticipated prices fluctuate randomly”, Industrial Management Review, 6(2), 41-49. Sovbetov Y. (2018), “Factors influencing cryptocurrency prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero”, Journal of Economics and Financial Analysis\, 2(2), 1-27. Tasca P. (2016), “The dual nature of Bitcoin as a Payment Network and Money”, VI Chapter SUERF Conference Proceedings. Tiwari A., Jana K., Das D. and Roubaud D. (2018), “Informational efficiency of Bitcoin – An extension”, Economic Letters, 163, 106-109. Troster V., Tiwari A., Shahbaz M. and Macedo D. (2018), “Bitocin returns and risk: A general GARCH and GAS analysis”, Finance Research Letters, (forthcoming). Urquhart A (2016), “The inefficiency of Bitcoin”, Economics Letters, 148(C), 80-82. Wang S. and Vergne J. (2017), “Buzz factor or innovation potential: What explains cryptocurrencies’ returns?”, PLOS ONE, 12(5), Weber W. (2016), “Bitcoin and legitimacy crisis of money”, Cambridge Journal of Economics, 40(1), 17-41. Zivot E. and Andrews D. (1992), “Further evidence on the Great Crash, the oil-price shock, and the unit-root hypothesis”, Journal of Business and Economic Statistics, 10(3), 251-270. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/94712 |