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Cryptocurrencies and Market Efficiency


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1 Professor of Finance, Cochin University of Science and Technology, Kochi 682311, Kerala, India
     

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The exponential growth and volatility of cryptocurrencies have led to a global interest in crypto assets and their distribution as digital wealth. Currently, cryptocurrencies and tokenised gold are the two popular digital wealth. While tokenised gold has the backing of un-mined physical gold, crypto has the support of an ideology-driven trust with the patronage of a mining process using supercomputing power that rewards coins. Thus, the acceptance of cryptocurrency as a reliable digital wealth depends to a large extent on its price consistency and the related independence. Many researchers tested the price efficiency of Bitcoin for different time periods up to 2017. In most cases, inefficiencies of the coins were inferred by them. Furthermore, many studies focused on the long-run and short-run volatility dynamics of Bitcoins leaving the altcoins almost unexamined. Therefore, a fresh look at the price efficiency and the inter-relationships of Bitcoin and the three altcoins, which altogether have a market capitalization of nearly 80 per cent, is attempted here. In all, six rigorous tests for independence or random walk are applied on the daily returns of the coins. Bitcoin returns showed a random walk in four out of the six tests, Ethereum and Ripple in three tests, and Bitcoin Cash only in two tests. The strong symptoms of weak form of efficiency found at least in the case of Bitcoin can be considered as its acceptance as a digital wealth. While the daily returns of all the four cryptos have a long-run equilibrium, the daily prices of Bitcoin, Ethereum and Bitcoin Cash excluding Ripple showed cointegration among themselves. Nearly 88 per cent of the short-run fluctuations in Bitcoin returns are adjusted on a daily basis. Bitcoin Granger Cause Ethereum and Ripple, a bidirectional Granger Causality is found between Ripple and Ethereum. Thus, the acceptance of cryptos, especially Bitcoin, as a digital wealth option can increase in the future.

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  • Antonopoulos, A.M. (2017), Mastering Bitcoin: Programming the Open Blockchain (2nd Ed.), O'Reilly Media Inc., United States of America.
  • Baek, C. and M. Elbeck (2014), Bitcoins as an Investment or Speculative Vehicle?, Applied Economics Letters, 22(1): 30-34.
  • Bariviera, A.F. (2017), The Inefficiency of Bitcoin Revisited: A Dynamic Approach, Economics Letters, 161(C): 1-4.
  • Bartels, R. (1982), The Rank Version of Von Neumann’s Ratio Test for Randomness, Journal of American Statistical Association, 77(377): 40–46.
  • Bitcoin USD (BTC-USD) (2020), Historical Data, Yahoo! Finance, https://in.finance.yahoo.com/quote/BTC- USD/history?p=BTC-USD, Accessed 3 March 2020.
  • Blau, B.M. (2017), Price Dynamics and Speculative Trading in Bitcoin, Research in International Business and Finance, 41(3): 493-499.
  • Bouri, E., L.A. Gil‐Alana, R. Gupta and D. Roubaud (2019), Modelling Long Memory Volatility in the Bitcoin Market: Evidence of Persistance and Structural Breaks, International Journal of Finance & Economics, 24(1): 412-426.
  • Brauneis, A. and R. Mestel (2018), Price Discovery of Cryptocurrencies: Bitcoin and Beyond, Economics Letters, 165(C), 58-61.
  • Brock, W.A., W.D. Dechert, J.A. Schieinkman and B. LeBaron (1996), A Test for Independence Based on the Correlation Dimension, Econometric Review, 15(3): 197–235.
  • Chaim, P. and M.P. Laurini (2018), Volatility and Return Jumps in Bitcoin, Economics Letters, 173(C): 158-163.
  • Cheung, A., J.J. Su and E. Roca (2015), Crypto-currency Bubbles: An Application of the Phillips Shi-Yu (2013) methodology on Mt. Gox Bitcoin Prices, Applied Economic, 47(23): 2348–2358.
  • Ciaian, P., M. Rajcaniova and d. Kancs (2015), The Economics of BitCoin Price Formation, Applied Economics, 48(19): 1799-1815, November.
  • Corbet, S., B. Lucey, A. Urquhart and L. Yarovaya (2019), Cryptocurrencies as a Financial Asset: A Systematic Analysis, International Review of Financial Analysis, 62(C): 182-199.
  • Dwyer, G. (2015), The Economics of Bitcoin and Similar Private Digital Currencies, Journal of Financial Stability, 17(C): 81-91.
  • Engle, R.F. and C.W.J. Granger (1987), Co-Integration and Error Correction: Representation, Estimation and Testing, Econometrica, 55(2): 251–276.
  • Escanciano, J.C., I.N. Lobato (2009): An Automatic Portmanteau Test for Serial Correlation, Journal of Econometrics, 151(2): 140–149.
  • Fama, E.F (1995), Random Walks in Stock Market Prices, Financial Analysts Journal, 51(1): 75-80.
  • Fama, E.F (1970), Efficient Capital Markets: A Review of Theory and Empirical Work, Journal of Finance, 25(2): 383–417.
  • Fernandez-Villaverde, J. (2018), Cryptocurrencies: A Crash Course in Digital, The Australian Economic Review, 51(4): 514-526.
  • FINRA (2019), Bitcoin More Bit Risky, Retrieved from http://www.finra.org/investors/alerts/bitcoin-more-bit-risky
  • Fisher, D.E. and R.J. Jordan (1979), Security Analysis and Portfolio Management, (Second Edition Ed.), Englewood Cliffs, New Jersey: Prentice- Hall Inc.
  • Forbes (2019), Bitcoin Volatility Approached a 2019 Low in December, Retrieved from www.forbes.com:https://www.forbes.com/sites/cbovaird/2020/01/10/bitcoin-volatilityapproached-a-2019-low-in-december/#4ae9528b19fd
  • Gandal, N., J. Hamrick, T. Moore and T. Oberman (2018), Price Manipulation in the Bitcoin Ecosystem, Journal of Monetary Economics, 95(C): 86-96.
  • Gaurav, S. (2019), The Market for Cryptocurrencies: An Ode to Hayek, Economic and Political Weekly, 54(2): 12-15.
  • Granger, C.W.J. (1969), Investigating Causal Relations by Econometric Models and Crossspectral Methods, Econometrica, 37(3): 424 – 438.
  • Granger, C.W.J. and J. Lin (1995), Causality in the Long-run, Econometric Theory, 11(1): 530 – 536.
  • He, D. (2018), Monetary Policy in the Digital Age: Crypto Assests may One Day Reduce Demand for Central Bank Money, Finance & Development, 55(2): 13-16.
  • Hileman G. (2015), The Bitcoin Market Potential Index, In: Brenner M., Christin N., Johnson B., Rohloff K. (Eds.), Financial Cryptography and Data Security, FC 2015, Lecture Notes in Computer Science, Vol. 8976, Springer, Berlin, Heidelberg.
  • Horraa, L.P., G.d. Fuente and J. Perote (2019), The Drivers of Bitcoin Demand: A Short and Longrun Analysis, International Review of Financial Analysis, 62: 21-34.
  • Johansen, S. (1988), Statistical Analysis of Cointegration Vectors, Journal of Economic Dynamics, 12(2-3): 231- 254.
  • Kim, J.H. (2009), Automatic Variance Ratio Test under Conditional Heteroskedasticity, Finance Research Letters, 6(3): 179–185.
  • Ljung, G.M. and G.E.P. Box (1978), On a Measure of the Lack of Fit in Time Series Models, Biometrika, 65(2): 297-303.
  • Modigliani, F. and M. Miller (1958), The Cost of Capital, Corporation Finance and the Theory of Investment, American Economic Review, 48(3): 261–297.
  • Murali, J. (2013), A New Coinage: Can Bitcoin, the Global Online Digital Currency, be the Precursor of a New Monetary System? Economic and Political Weekly, 48(38): 77-78.
  • Nadarajah, S., and J. Chu (2016), On the Inefficiency of Bitcoin, Economics Letters, 150: 6-9, October.
  • Nakamoto, S. (2008), Bitcoin: A Peer-to-Peer Electronic Cash System, https://Bitcoin.org/Bitcoin.pdf.
  • Saal, M., S. Starnes and T. Rehermann (2017), Digital Financial Services: Challenges and Opportunities for Emerging Market Banks, EM Compass, International Finance Corporation, World Bank Group, August.
  • Shen, D., A. Urquhart and P. Wang (2019), Does Twitter Predict Bitcoin? Economics Letters, 174: 118-123.
  • Sotiropoulou, A. and D. Gue´gan (2017), Bitcoin and the Challenges for Financial Regulation, Capital Markets Law Journal, 12(4): 466-479, September.
  • Tiwari, A.K., R. Jana, D. Das and D. Roubaud (2017), Informational Efficiency of Bitcoin - An Extension, Economics Letters, 163: 106-109.
  • Urquhart, A. (2016), The Inefficiency of Bitcoin, Economic Letters, 148: 80–82.
  • Urquhart, A. (2017), Price Clustering in Bitcoin, Economic Letters, 159: 145–148.
  • Vidal-Tomas, D. and A. Ibanez (2018), Semi- strong Efficiency of Bitcoin, Finance Research Letters, https://doi.org/10.1016/j.frl.2018.03.013
  • Wald, A. and J. Wolfowtiz (1940), On a Test Whether Two Samples are form the Same Population, Annals of Mathematical Statistics, 11(2): 147–162.
  • Weber, B. (2014), Bitcoin and the Legitimacy Crisis of Money, Cambridge Journal of Economics, 40(1): 17–41.
  • Wikipedia Contributors (2020), Bitcoin, In Wikipedia, The Free Encyclopaedia, Retrieved 05:43, March 22, 2020, from https://en.wikipedia.org/w/index.php?title=Bitcoin&oldid= 946643828

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  • Cryptocurrencies and Market Efficiency

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Authors

S. Santhosh Kumar
Professor of Finance, Cochin University of Science and Technology, Kochi 682311, Kerala, India

Abstract


The exponential growth and volatility of cryptocurrencies have led to a global interest in crypto assets and their distribution as digital wealth. Currently, cryptocurrencies and tokenised gold are the two popular digital wealth. While tokenised gold has the backing of un-mined physical gold, crypto has the support of an ideology-driven trust with the patronage of a mining process using supercomputing power that rewards coins. Thus, the acceptance of cryptocurrency as a reliable digital wealth depends to a large extent on its price consistency and the related independence. Many researchers tested the price efficiency of Bitcoin for different time periods up to 2017. In most cases, inefficiencies of the coins were inferred by them. Furthermore, many studies focused on the long-run and short-run volatility dynamics of Bitcoins leaving the altcoins almost unexamined. Therefore, a fresh look at the price efficiency and the inter-relationships of Bitcoin and the three altcoins, which altogether have a market capitalization of nearly 80 per cent, is attempted here. In all, six rigorous tests for independence or random walk are applied on the daily returns of the coins. Bitcoin returns showed a random walk in four out of the six tests, Ethereum and Ripple in three tests, and Bitcoin Cash only in two tests. The strong symptoms of weak form of efficiency found at least in the case of Bitcoin can be considered as its acceptance as a digital wealth. While the daily returns of all the four cryptos have a long-run equilibrium, the daily prices of Bitcoin, Ethereum and Bitcoin Cash excluding Ripple showed cointegration among themselves. Nearly 88 per cent of the short-run fluctuations in Bitcoin returns are adjusted on a daily basis. Bitcoin Granger Cause Ethereum and Ripple, a bidirectional Granger Causality is found between Ripple and Ethereum. Thus, the acceptance of cryptos, especially Bitcoin, as a digital wealth option can increase in the future.

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DOI: https://doi.org/10.21648/arthavij%2F2021%2Fv63%2Fi1%2F208209