Abstract
This chapter examines the time-varying causal relationship between trading volume and returns in cryptocurrency markets. The chapter employs a novel Granger causality framework based on a recursive evolving window procedure. The procedures allow detecting changes in causal relationships among time series by considering potential conditional heteroskedasticity and structural shifts through recursive subsampling. The chapter analyzes the return-volume relationship for Bitcoin and seven other altcoins: Dash, Ethereum, Litecoin, Nem, Stellar, Monero, and Ripple. The results suggest rejecting the null hypothesis of no causality, indicating bi-directional causality between trading volume and returns for Bitcoin and the altcoins except Nem and Stellar. The findings also highlight that the causal relations in cryptocurrency markets are subject to change over time. The chapter may conclude that trading volume has predictive power on returns in cryptocurrency markets, implying potential benefits of constructing volume-based trading strategies for investors and considering trading volume information in developing pricing models to determine the fundamental value of the cryptocurrencies.
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References
Aalborg, H. A., Molnár, P., & de Vries, J. E. (2018). What can explain the price, volatility and trading volume of bitcoin? Finance Research Letters, 29, 255–265. https://doi.org/10.1016/J.FRL.2018.08.010
Al-Yahyaee, K. H., Mensi, W., & Yoon, S.-M. (2018). Efficiency, multifractality, and the long-memory property of the bitcoin market: A comparative analysis with stock, currency, and gold markets. Finance Research Letters, 27, 228–234. https://doi.org/10.1016/J.FRL.2018.03.017
Balcilar, M., Bouri, E., Gupta, R., & Roubaud, D. (2017). Can volume predict bitcoin returns and volatility? A quantiles-based approach. Economic Modelling, 64, 74–81. https://doi.org/10.1016/J.ECONMOD.2017.03.019
Bariviera, A. F. (2017). The inefficiency of bitcoin revisited: A dynamic approach. Economics Letters, 161, 1–4. https://doi.org/10.1016/J.ECONLET.2017.09.013
Baur, D. G., & Dimpfl, T. (2018). Price discovery in bitcoin spot or futures? SSRN. https://doi.org/10.2139/ssrn.3171464
Becker, J., Breuker, D., Heide, T., Holler, J., Rauer, H. P., & Böhme, R. (2013). Can we afford integrity by proof-of-work? Scenarios inspired by the bitcoin currency. In The economics of information security and privacy (pp. 135–156). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-39498-0_7
Beneki, C., Koulis, A., Kyriazis, N. A., & Papadamou, S. (2019). Investigating volatility transmission and hedging properties between bitcoin and Ethereum. Research in International Business and Finance, 48, 219–227. https://doi.org/10.1016/J.RIBAF.2019.01.001
Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213–238. https://doi.org/10.1257/jep.29.2.213
Bouoiyour, J., & Refk, S. (2015). What does bitcoin look like? Annals of Economics and Finance, 16(2), 449–192. Retrieved from http://aeconf.com/Articles/Nov2015/aef160211.pdf
Bouri, E., Gupta, R., Tiwari, A. K., & Roubaud, D. (2017). Does bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Finance Research Letters, 23, 87–95. https://doi.org/10.1016/J.FRL.2017.02.009
Bouri, E., Jalkh, N., Molnár, P., & Roubaud, D. (2017). Bitcoin for energy commodities before and after the December 2013 crash: Diversifier, hedge or safe haven? Applied Economics, 49(50), 1–11. https://doi.org/10.1080/00036846.2017.1299102
Bouri, E., Lau, C. K. M., Lucey, B., & Roubaud, D. (2018). Trading volume and the predictability of return and volatility in the cryptocurrency market. Finance Research Letters., 29, 340–346. https://doi.org/10.1016/j.frl.2018.08.015
Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of bitcoin: Is it really more than a diversifier? Finance Research Letters, 20, 192–198. https://doi.org/10.1016/J.FRL.2016.09.025
Brandvold, M., Molnár, P., Vagstad, K., & Andreas Valstad, O. C. (2015). Price discovery on bitcoin exchanges. Journal of International Financial Markets, Institutions and Money, 36, 18–35. https://doi.org/10.1016/J.INTFIN.2015.02.010
Brauneis, A., & Mestel, R. (2018). Price discovery of cryptocurrencies: Bitcoin and beyond. Economics Letters, 165, 58–61. https://doi.org/10.1016/J.ECONLET.2018.02.001
Brière, M., Oosterlinck, K., & Szafarz, A. (2015). Virtual currency, tangible return: Portfolio diversification with bitcoin. Journal of Asset Management, 16(6), 365–373. https://doi.org/10.1057/jam.2015.5
Cagli, E. C. (2018). Explosive behavior in the prices of bitcoin and altcoins. Finance Research Letters., 29, 398–403. https://doi.org/10.1016/j.frl.2018.09.007
Cheah, E.-T., & Fry, J. (2015). Speculative bubbles in bitcoin markets? An empirical investigation into the fundamental value of bitcoin. Economics Letters, 130, 32–36. https://doi.org/10.1016/J.ECONLET.2015.02.029
Cheung, A., Roca, E., & Su, J.-J. (2015). Crypto-currency bubbles: An application of the Phillips–Shi–Yu (2013) methodology on Mt. Gox bitcoin prices. Applied Economics, 47(23), 2348–2358. https://doi.org/10.1080/00036846.2015.1005827
Ciaian, P., Rajcaniova, M., & Kancs, D. (2016). The economics of BitCoin price formation. Applied Economics, 48(19), 1799–1815. https://doi.org/10.1080/00036846.2015.1109038
Clark, P. K. (1973). A subordinated stochastic process model with finite variance for speculative prices. Econometrica, 41(1), 135–155. https://doi.org/10.2307/1913889
Copeland, T. E. (1976). A model of asset trading under the assumption of sequential information arrival. The Journal of Finance, 31(4), 1149–1168. https://doi.org/10.1111/j.1540-6261.1976.tb01966.x
Copeland, T. E. (1977). A probability model of asset trading. The Journal of Financial and Quantitative Analysis, 12(4), 563–578. https://doi.org/10.2307/2330332
Corbet, S., Lucey, B., & Yarovaya, L. (2018). Datestamping the bitcoin and Ethereum bubbles. Finance Research Letters, 26, 81–88. https://doi.org/10.1016/j.frl.2017.12.006
Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28–34. https://doi.org/10.1016/J.ECONLET.2018.01.004
Crouch, R. L. (1970). The volume of transactions and price changes on the New York stock exchange. Financial Analysts Journal, 26(4), 104–109.
De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of Political Economy, 98(4), 703–738. https://doi.org/10.1086/261703
Demir, E., Gozgor, G., Lau, C. K. M., & Vigne, S. A. (2018). Does economic policy uncertainty predict the bitcoin returns? An empirical investigation. Finance Research Letters, 26, 145–149. https://doi.org/10.1016/j.frl.2018.01.005
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.2307/2286348
Dwyer, G. P. (2015). The economics of bitcoin and similar private digital currencies. Journal of Financial Stability, 17, 81–91. https://doi.org/10.1016/J.JFS.2014.11.006
Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar – A GARCH volatility analysis. Finance Research Letters, 16, 85–92. https://doi.org/10.1016/J.FRL.2015.10.008
El Alaoui, M., Bouri, E., & Roubaud, D. (2018). Bitcoin price–volume: A multifractal cross-correlation approach. Finance Research Letters.https://doi.org/10.1016/J.FRL.2018.12.011
Epps, T. W., & Epps, M. L. (1976). The stochastic dependence of security price changes and transaction volumes: Implications for the mixture-of-distributions hypothesis. Econometrica, 44(2), 305–321. https://doi.org/10.2307/1912726
Feng, W., Wang, Y., & Zhang, Z. (2018). Can cryptocurrencies be a safe haven: A tail risk perspective analysis. Applied Economics, 50(44), 4745–4762. https://doi.org/10.1080/00036846.2018.1466993
Fry, J., & Cheah, E.-T. (2016). Negative bubbles and shocks in cryptocurrency markets. International Review of Financial Analysis, 47, 343–352. https://doi.org/10.1016/J.IRFA.2016.02.008
Gallant, A. R., Rossi, P. E., & Tauchen, G. (1992). Stock prices and volume. Review of Financial Studies, 5(2), 199–242. https://doi.org/10.1093/rfs/5.2.199
Garcia, D., Tessone, C. J., Mavrodiev, P., & Perony, N. (2014). The digital traces of bubbles: Feedback cycles between socio-economic signals in the bitcoin economy. Journal of the Royal Society Interface, 11(99), 20140623. https://doi.org/10.1098/rsif.2014.0623
Gebka, B. (2012). The dynamic relation between returns, trading volume, and volatility: Lessons from spillovers between Asia and the United States. Bulletin of Economic Research, 64(1), 65–90. https://doi.org/10.1111/j.1467-8586.2010.00371.x
Gebka, B., & Wohar, M. E. (2013). Causality between trading volume and returns: Evidence from quantile regressions. International Review of Economics & Finance, 27, 144–159. https://doi.org/10.1016/J.IREF.2012.09.009
Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M. C., & Siering, M. (2014). Bitcoin - asset or currency? Revealing users’ hidden intentions. In Twenty second European conference on information systems (pp. 1–14). Tel Aviv. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2425247
Harris, L. (1986). Cross-security tests of the mixture of distributions hypothesis. The Journal of Financial and Quantitative Analysis, 21(1), 39–46. https://doi.org/10.2307/2330989
Harris, M., & Raviv, A. (1993). Differences of opinion make a horse race. Review of Financial Studies, 6(3), 473–506. https://doi.org/10.1093/rfs/6.3.473
He, H., & Wang, J. (1995). Differential information and dynamic behavior of stock trading volume. Review of Financial Studies, 8(4), 919–972. https://doi.org/10.1093/rfs/8.4.919
Hiemstra, C., & Jones, J. D. (1994). Testing for linear and nonlinear granger causality in the stock price-volume relation testing for linear and nonlinear granger causality in the stock price-volume relation. The Journal of Finance, 49(5), 1639–1664. https://doi.org/10.1111/j.1540-6261.1994.tb04776.x
Jennings, R. H., Starks, L. T., & Fellingham, J. C. (1981). An equilibrium model of asset trading with sequential information arrival. The Journal of Finance, 36(1), 143–161. https://doi.org/10.1111/j.1540-6261.1981.tb03540.x
Jiang, Y., Nie, H., & Ruan, W. (2018). Time-varying long-term memory in bitcoin market. Finance Research Letters, 25, 280–284. https://doi.org/10.1016/j.frl.2017.12.009
Kandel, E., & Pearson, N. D. (1995). Differential interpretation of public signals and trade in speculative markets. Journal of Political Economy, 103(4), 831–872. https://doi.org/10.1086/262005
Kapar, B., & Olmo, J. (2019). An analysis of price discovery between bitcoin futures and spot markets. Economics Letters, 174, 62–64. https://doi.org/10.1016/J.ECONLET.2018.10.031
Karpoff, J. M. (1987). The relation between price changes and trading volume: A survey. The Journal of Financial and Quantitative Analysis, 22(1), 109–126. https://doi.org/10.2307/2330874
Kokkinaki, A., Sapuric, S., & Georgiou, I. (2019). The relationship between bitcoin trading volume, volatility, and returns: A study of four seasons. In M. Themistocleous & P. R. da Cunha (Eds.), 15th European, Mediterranean, and Middle Eastern Conference, EMCIS (Vol. 2018, pp. 3–15). Limassol, Cyprus: Springer. https://doi.org/10.1007/978-3-030-11395-7_1
Koutmos, D. (2018). Bitcoin returns and transaction activity. Economics Letters, 167, 81–85. https://doi.org/10.1016/J.ECONLET.2018.03.021
Kristoufek, L. (2013). BitCoin meets Google trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era. Scientific Reports, 3(3415), 1–7. Retrieved from https://www.nature.com/articles/srep03415%3FWT.ec_id%3DSREP-20131210
Kristoufek, L. (2015). What are the main drivers of the bitcoin price? Evidence from wavelet coherence analysis. PLoS One, 10(4), e0123923. https://doi.org/10.1371/journal.pone.0123923
Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica, 53(6), 1315–1335. https://doi.org/10.2307/1913210
Lakonishok, J., & Smidt, S. (1989). Past price changes and current trading volume. The Journal of Portfolio Management, 15(4), 18–24. https://doi.org/10.3905/jpm.1989.409223
Llorente, G., Michaely, R., Saar, G., & Wang, J. (2002). Dynamic volume-return relation of individual stocks. Review of Financial Studies, 15(4), 1005–1047. https://doi.org/10.1093/rfs/15.4.1005
Nadarajah, S., & Chu, J. (2017). On the inefficiency of bitcoin. Economics Letters, 150, 6–9. https://doi.org/10.1016/J.ECONLET.2016.10.033
Pagnottoni, P., Dimpfl, T., & Baur, D. (2018). Price discovery on bitcoin markets. SSRN Electronic Journal.https://doi.org/10.2139/ssrn.3280261
Panagiotidis, T., Stengos, T., & Vravosinos, O. (2018). On the determinants of bitcoin returns: A LASSO approach. Finance Research Letters, 27, 235–240. https://doi.org/10.1016/j.frl.2018.03.016
Phillips, P. C. B., Shi, S., & Yu, J. (2015). Testing for multiple bubbles: Historical episodes of exuberance and collapse in the S&P 500. International Economic Review, 56(4), 1043–1078. https://doi.org/10.1111/iere.12132
Plassaras, N. A. (2013). Regulating digital currencies: Bringing bitcoin within the reach of the IMF. Chicago Journal of International Law, 14(1), 377–407.
Rogojanu, A., & Badea, L. (2014). The issue of competing currencies. Case study-bitcoin. Theoretical & Applied Economics, 21(1), 103–114.
Sadeghi, A.-R. (Ed.). (2013). Financial cryptography and data security (Vol. 7859). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-39884-1
Schneider, J. (2009). A rational expectations equilibrium with informative trading volume. The Journal of Finance, 64(6), 2783–2805. https://doi.org/10.1111/j.1540-6261.2009.01517.x
Sensoy, A. (2018). The inefficiency of bitcoin revisited: A high-frequency analysis with alternative currencies. Finance Research Letters, 28, 68–73. https://doi.org/10.1016/J.FRL.2018.04.002
Shahzad, S. J. H., Bouri, E., Roubaud, D., Kristoufek, L., & Lucey, B. (2019). Is bitcoin a better safe-haven investment than gold and commodities? International Review of Financial Analysis., 63, 322–330. https://doi.org/10.1016/J.IRFA.2019.01.002
Shi, S., Phillips, P. C. B., & Hurn, S. (2018). Change detection and the causal impact of the yield curve. Journal of Time Series Analysis, 39(6), 966–987. https://doi.org/10.1111/jtsa.12427
Stosic, D., Stosic, D., Ludermir, T. B., & Stosic, T. (2019). Multifractal behavior of price and volume changes in the cryptocurrency market. Physica A: Statistical Mechanics and Its Applications, 520, 54–61. https://doi.org/10.1016/J.PHYSA.2018.12.038
Swanson, N. R. (1998). Money and output viewed through a rolling window. Journal of Monetary Economics, 41(3), 455–474. https://doi.org/10.1016/S0304-3932(98)00005-1
Tauchen, G. E., & Pitts, M. (1983). The price variability-volume relationship on speculative markets. Econometrica, 51(2), 485–505. https://doi.org/10.2307/1912002
Thoma, M. A. (1994). Subsample instability and asymmetries in money-income causality. Journal of Econometrics, 64(1–2), 279–306. https://doi.org/10.1016/0304-4076(94)90066-3
Tiwari, A. K., Jana, R. K., Das, D., & Roubaud, D. (2018). Informational efficiency of bitcoin—An extension. Economics Letters, 163, 106–109. https://doi.org/10.1016/J.ECONLET.2017.12.006
Urquhart, A. (2016). The inefficiency of bitcoin. Economics Letters, 148, 80–82. https://doi.org/10.1016/J.ECONLET.2016.09.019
van Wijk, D. (2013). What can be expected from the BitCoin (no. 345986). Rotterdam.
Vidal-Tomás, D., & Ibañez, A. (2018). Semi-strong efficiency of bitcoin. Finance Research Letters, 27, 259–265. https://doi.org/10.1016/J.FRL.2018.03.013
Yermack, D. (2015). Is bitcoin a real currency? An economic appraisal. In D. K. C. Lee (Ed.), Handbook of digital currency (pp. 31–43). London: Academic. https://doi.org/10.1016/B978-0-12-802117-0.00002-3
Ying, C. C. (1966). Stock market prices and volumes of sales. Econometrica, 34(3), 676–685. https://doi.org/10.2307/1909776
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Cagli, E.C. (2019). The Causal Relationship Between Returns and Trading Volume in Cryptocurrency Markets: Recursive Evolving Approach. In: Hacioglu, U. (eds) Blockchain Economics and Financial Market Innovation. Contributions to Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-25275-5_9
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