Can volume predict Bitcoin returns and volatility? A quantiles-based approach

Economic Modelling - Tập 64 - Trang 74-81 - 2017
Mehmet Balcilar1,2,3, Elie Bouri4, Rangan Gupta3, David Roubaud2
1Eastern Mediterranean University, Famagusta, via Mersin 10, Northern Cyprus, Turkey
2Montpellier Business School, 2300 Avenue des Moulins, 34080, Montpellier, France
3Department of Economics, University of Pretoria, Pretoria, 0002, South Africa
4USEK Business School, Holy Spirit University of Kaslik, PO BOX 446, Jounieh, Lebanon

Tài liệu tham khảo

Bai, 2003, Computation and analysis of multiple structural change models, J. Appl. Econ., 18, 1, 10.1002/jae.659 Balcilar, 2016, Does economic policy uncertainty predict exchange rate returns and volatility? Evidence from a nonparametric causality-in-quantiles test, Open Econ. Rev., 27, 229, 10.1007/s11079-016-9388-x Balcilar, 2016, Terror attacks and stock market fluctuations: evidence based on a nonparametric causality-in-quantiles test for the G7 countries, Eur. J. Financ., 10.1080/1351847X.2016.1239586 Balduzzi, 2001, Economic news and bond prices: evidence from the U.S. Treasury market, J. Financ. Quant. Anal., 36, 523, 10.2307/2676223 Bampinas, 2015, On the relationship between oil and gold before and after financial crisis: linear, nonlinear and time-varying causality testing, Stud. Nonlinear Dyn. Econ., 19, 657 Bekiros, 2016, Incorporating economic policy uncertainty in US equity premium models: a nonlinear predictability analysis, Financ. Res. Lett., 18, 291, 10.1016/j.frl.2016.01.012 Bouoiyour, 2015, What Bitcoin looks like?, Ann. Econ. Financ., 16, 449 Bouoiyour, 2015, Is Bitcoin business income or speculative foolery? New ideas through an improved frequency domain analysis, Ann. Financ. Econ., 10, 1550002, 10.1142/S2010495215500025 Bouri, E., Gil-Alana, L.A., Gupta, R., Roubaud, D., 2016. Modelling Long Memory Volatility in the Bitcoin Market: Evidence of Persistence and Structural Breaks. Department of Economics, University of Pretoria, Working Paper No. 2016-54. Bouri, 2017, On the return-volatility relationship in the Bitcoin market around the price crash of 2013, Econ.: Open-Access Open-Assess. E-J., 11, 1 Bouri, 2017, On the hedge and safe haven properties of Bitcoin: is it really more than a diversifier?, Financ. Res. Lett., 20, 192, 10.1016/j.frl.2016.09.025 Brandvold, 2015, Price discovery on Bitcoin exchanges, J. Int. Financ. Mark. Inst. Money, 36, 18, 10.1016/j.intfin.2015.02.010 Brock, 1996, A test for independence based on the correlation dimension, Econ. Rev., 15, 197, 10.1080/07474939608800353 Cheah, 2015, Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin, Econ. Lett., 130, 32, 10.1016/j.econlet.2015.02.029 Chen, 2001, The dynamic relation between stock returns, trading volume and volatility, Financ. Rev., 36, 153, 10.1111/j.1540-6288.2001.tb00024.x Chen, 2016, Evidence of stock returns and abnormal trading volume: a threshold quantile regression approach, Jpn. Econ. Rev., 67, 96, 10.1111/jere.12074 Chiang, 2012, Stock returns and risk: evidence from quantile regression analysis, J. Risk Financ. Manag., 5, 20, 10.3390/jrfm5010020 Chiarella, 2016, The return-volatility relation in commodity futures markets, J. Futur. Mark., 36, 127, 10.1002/fut.21717 Chuang, 2009, Causality in quantiles and dynamic stock return-volume relations, J. Bank. Financ., 33, 1351, 10.1016/j.jbankfin.2009.02.013 Ciaian, 2016, The economics of BitCoin price formation, ‎Appl. Econ., 48, 1799, 10.1080/00036846.2015.1109038 Clark, 1973, A subordinated stochastic process model with finite variance for speculative prices, Economics, 41, 135, 10.2307/1913889 Copeland, 1976, A model of asset trading under the assumption of sequential information arrival, J. Financ., 31, 1149, 10.2307/2326280 Diks, 2005, A note on the Hiemstra-Jones test for Granger non-causality, Stud. Nonlinear Dyn. Econ., 9, 1 Dwyer, 2015, The economics of Bitcoin and similar private digital currencies, J. Financ. Stab., 17, 81, 10.1016/j.jfs.2014.11.006 Fry, 2016, Negative bubbles and shocks in cryptocurrency markets, Int. Rev. Financ. Anal., 47, 343, 10.1016/j.irfa.2016.02.008 Gallant, 1992, Stock prices and volume, Rev. Financ. Stud., 5, 199, 10.1093/rfs/5.2.199 Garcia, 2014, The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy, J. R. Soc. Interface, 11, 1, 10.1098/rsif.2014.0623 Gebka, 2012, The dynamic relation between returns, trading volume, and volatility: lessons from spillovers between Asia and the United States, Bull. Econ. Res., 64, 65, 10.1111/j.1467-8586.2010.00371.x Gebka, 2013, Causality between trading volume and returns: evidence from quantile regressions, Int. Rev. Econ. Financ., 27, 144, 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. Revealing Users' Hidden Intentions. ECIS. Available at SSRN: 2425247. Hanley, B.P., 2013. The False Premises and Promises of Bitcoin. arXiv preprint arXiv:1312.2048. Hayes, 2016, Cryptocurrency value formation: an empirical study leading to a cost of production model for valuing Bitcoin, Telemat. Inform. Hiemstra, 1994, Testing for linear and nonlinear Granger causality in the stock price-volume relation, J. Financ., 49, 1639 Hill, 2007, Efficient tests of long-run causation in trivariate VAR processes with a rolling window study of the money-income relationship, J. Appl. Econ., 22, 747, 10.1002/jae.925 Hurvich, 1989, Regression and time series model selection in small samples, Biometrika, 76, 297, 10.1093/biomet/76.2.297 Jeong, 2012, A consistent nonparametric test for causality in quantile, Econ. Theor., 28, 861, 10.1017/S0266466611000685 Karpoff, 1987, The relation between price changes and trading volume: a survey, J. Financ. Quant. Anal., 22, 109, 10.2307/2330874 Kristoufek, L., 2013. BitCoin Meets Google Trends and Wikipedia: Quantifying the Relationship between Phenomena of the Internet Era. Scientific Reports 3, Article number: 3415. http://dx.doi.org/10.1038/srep03415. Kristoufek, 2014, What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis, PLoS One, 10, e0123923, 10.1371/journal.pone.0123923 Li, 2004, Cross-validated local linear nonparametric regression, Stat. Sin., 14, 485 Li, 2016, Generalized cross-spectral test for nonlinear Granger causality with applications to money–output and price–volume relations, Econ. Model., 52, 661, 10.1016/j.econmod.2015.09.037 Lin, 2013, Dynamic stock return-volume relation: evidence from emerging Asian markets, Bull. Econ. Res., 65, 178, 10.1111/j.1467-8586.2011.00428.x Marsh, T.A., Wagner, N., 2000. Return-volume dependence and extremes in international markets. Working Paper RPF-293, Research Program in Finance, University of California, Berkeley. Nishiyama, 2011, A consistent nonparametric Test for nonlinear causality – specification in time series regression, J. Econ., 165, 112, 10.1016/j.jeconom.2011.05.010 Polasik, 2015, Price fluctuations and the use of Bitcoin: an empirical inquiry, Int. J. Electron. Commun., 20, 9, 10.1080/10864415.2016.1061413 Popper, 2015 Puri, 2008, Asymmetric volume-return relation and concentrated trading in LIFFE futures, Eur. Financ. Manag., 14, 528, 10.1111/j.1468-036X.2007.00396.x Racine, 2004, Nonparametric estimation of regression functions with both categorical and continuous data, J. Econ., 119, 99, 10.1016/S0304-4076(03)00157-X Todorova, 2014, The impact of trading volume, number of trades and overnight returns on forecasting the daily realized range, Econ. Model., 36, 332, 10.1016/j.econmod.2013.10.003 Tsai, 2014, Ripple effect in house prices and trading volume in the UK housing market: new viewpoint and evidence, Econ. Model., 40, 68, 10.1016/j.econmod.2014.03.026 Woo, 2013 Yermack, D., 2013. Is Bitcoin A Real Currency? An Economic Appraisal (No. w19747). National Bureau of Economic Research. URL: 〈http://www.nber.org/papers/w19747〉.