References
- Polasik M, Piotrowska A, Wisniewski T, Kotkowski R, Lightfoot G. Price fluctuations and the use of bitcoin: an empirical inquiry. Int J Electron Commerce. 2015;20(1):9–49. doi:https://doi.org/10.1080/10864415.2016.1061413.
- Baur D, Hong K, Lee A. Bitcoin: medium of exchange or speculative assets? J Int Financl Markets Inst Money. 2018;54:177–89. doi:https://doi.org/10.1016/j.intfin.2017.12.004.
- Corbet S, Larkin C, Lucey B, Meegan A, Yarovaya L. The impact of macroeconomic news on Bitcoin returns. Eur J Finance. 2020;26:1396–416.
- Zhang W, Wang P, Shen D. The inefficiency of cryptocurrency and its cross-correlation with Dow Jones industrial average. Phys A: Stat Mech Appl. 2018;510:658–70. doi:https://doi.org/10.1016/j.physa.2018.07.032.
- Fama EF. Efficient capital markets: a review of theory and empirical work. J Finance. 1970;25(2):383–417. doi:https://doi.org/10.2307/2325486.
- Pichl L, Kaizoji T. Volatility analysis of Bitcoin price time series. Quant Finance Econ. 2017;1:474–85. doi:https://doi.org/10.3934/QFE.2017.4.474.
- Vo A, Yost-Bremm C. A high-frequency algorithmic trading strategy for cryptocurrency. J Comput Inf Syst. 2020;60:555–568.
- Klein T, Thu H, Walther T. Bitcoin is not the New Gold–A comparison of volatility, correlation, and portfolio performance. Int Rev Financ Anal. 2018;59:105–16. doi:https://doi.org/10.1016/j.irfa.2018.07.010.
- Madan I, Saluja S, Zhao A. Automated bitcoin trading via machine learning algorithms. Stanford (CA): Stanford University; 2015.
- Sin E, Wang L. Bitcoin price prediction using ensembles of neural networks. 13th International Conference on Natural Computation, Fuzzy Systems, and Knowledge Discovery (ICNC-FSKD). San Francisco, CA: IEEE; 2017. p. 666–71.
- Greaves A, Au B. Using the Bitcoin transaction graph to predict the price of Bitcoin. Unknown; 2015.
- Jang H, Lee J. An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information. IEEE Access. 2017;6:5427–37. doi:https://doi.org/10.1109/ACCESS.2017.2779181.
- McNally S, Roche J. Predicting the price of Bitcoin using machine learning. (Doctoral dissertation), Dublin: National College of Ireland, School of Computing; 2016.
- Azari A. Bitcoin price prediction: an ARIMA approach. arXiv preprint arXiv; 2019.
- Wu C, Lu C, Ma Y, Lu R. A new forecasting framework for bitcoin price with LSTM. IEEE International Conference on Data Mining Workshops (ICDMW). Singapore: IEEE; 2018. p. 168–75.
- Kaminski JC. Nowcasting the Bitcoin market with twitter signals. Cambridge MA: MIT Media Lab; 2016.
- Linton M, Teo EG, Bommes E, Chen CY, Härdle WK. Dynamic topic modelling for cryptocurrency community forums. In Applied quantitative finance. Berlin, Heidelberg: Springer; 2017; p. 355–372.
- Kim Y, Lee J, Park N, Choo J, Kim J, Kim C. When Bitcoin encounters information in an online forum: using text mining to analyse user opinions and predict value fluctuation. PloS One. 2017;12(5):e0177630.
- Kristoufek L. Bitcoin meets Google Trends and Wikipedia: quantifying the relationship between phenomena of the Internet era. Sci Rep. 2013;3(1):1–7. doi:https://doi.org/10.1038/srep03415.
- Ciaian P, Rajcaniova M, Kancs D. The economics of BitCoin price formation. Appl Econ. 2016;48(19):1799–815. doi:https://doi.org/10.1080/00036846.2015.1109038.
- Zhu Y, Dickinson D, Li J. Analysis on the influence factors of Bitcoin’s price based on VEC model. Financ Innovation. 2017;3(1):3. doi:https://doi.org/10.1186/s40854-017-0054-0.
- Baek C, Elbeck M. Bitcoins as an investment or speculative vehicle? A first look. Appl Econ Lett. 2015;22(1):30–34. doi:https://doi.org/10.1080/13504851.2014.916379.
- Goczek K, Skliarov I. What drives the bitcoin price? A factor augmented error correction mechanism investigation. Appl Econ. 2019;51(59):6393–410. doi:https://doi.org/10.1080/00036846.2019.1619021.
- Bouri E, Gupta R. Predicting Bitcoin Returns: comparing the roles and newspaper and internet search-based measures of uncertainty. Finance Res Lett. 2019;101398.
- Bouri E, Gupta R, Tiwari A, Roubad D. Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-on-quantile regressions. Finance Res Lett. 2017;23:87–95. doi:https://doi.org/10.1016/j.frl.2017.02.009.
- Bouri E, Molnar P, Azzi G, Roubaud D, Hagfors L. On the hedge and safe haven properties of Bitcoin: is it really more than a diversifier? Finance Res Lett. 2017;20:192–98. doi:https://doi.org/10.1016/j.frl.2016.09.025.
- Shahzad S, Bouri E, Roubad D, Kristoufek L, Lucey B. Is Bitcoin a better safe-haven investment than gold and commodities. Int Rev Financ Anal. 2019;63:322–30. doi:https://doi.org/10.1016/j.irfa.2019.01.002.
- Dyhrberg A. Bitcoin, gold, and the dollar - A GARCH volatility analysis. Finance Res Lett. 2016;16:85–92. doi:https://doi.org/10.1016/j.frl.2015.10.008.
- Radityo A, Munajat Q, Budi I. Prediction of Bitcoin exchange rate to American dollar using artificial neural network methods. International Conference on Advanced Computer Science and Information Systems (ICACSIS); Bali, Indonesia: IEEE. 2017. p. 433–38.
- Roy S, Nanjiba S, Chakrabarty A. Bitcoin price forecasting using time series analysis. 21st International Conference of Computer and Information Technology (ICCIT). Badda, Dhaka 1212, Bangladesh: IEEE; 2018. p. 1–5.
- Frias A, Freire E. Financial shielding that Bitcoin grants to capitals in the world. Investment Manage Finanl Innovations. 2019;16(3):49. doi:https://doi.org/10.21511/imfi.16(3).2019.06.
- Li X, Wang C. The technology and economic determinants of cryptocurrency exchange rates: the case of Bitcoin. Decis Support Syst. 2017;95:49–60. doi:https://doi.org/10.1016/j.dss.2016.12.001.
- Chen N, Roll R, Ross S. Economic forces and the stock market. J Bus. 1986;59(3):383–403. doi:https://doi.org/10.1086/296344.
- Shanken J, Weinstein M. Economic forces and the stock market revisited. J Empirical Finance. 2006;13(2):129–44. doi:https://doi.org/10.1016/j.jempfin.2005.09.001.
- Vassiliadis S, Papadopoulos P, Rangoussi M, Konieczny T, Gralewski J. Bitcoin value analysis based on cross-correlations. J Internet Banking Commerce. 2017;22:1.
- Kristjanpoller W, Bouri E, Takaishi T. Cryptocurrencies and equity funds: evidence from an asymmetric multifractal analysis. Phys A: Stat Mech Appl. 2020;545:123711. doi:https://doi.org/10.1016/j.physa.2019.123711.
- Selmi R, Mensi W, Hammoudeh S, Bouoiyour J. Is Bitcoin a hedge, a safe haven or a diversifier for oil price movements? A comparison with gold. Energy Econ. 2018;74:787–801. doi:https://doi.org/10.1016/j.eneco.2018.07.007.
- Jin J, Yu J, Hu Y, Shang Y. Which one is more informative in determining price movements of hedging assets? Evidence from Bitcoin, gold and crude oil markets. Phys A: Stat Mech Appl. 2019;527:121121. doi:https://doi.org/10.1016/j.physa.2019.121121.
- Okorie DI, Lin B. Crude oil price and cryptocurrencies: evidence of volatility connectedness and hedging strategy. Energy Econ. 2020;87:104703. doi:https://doi.org/10.1016/j.eneco.2020.104703.
- Narayan PK, Narayan S, Rahman RE, Setiawan I. Bitcoin price growth and Indonesia’s monetary system. Emerging Markets Rev. 2019;38:364–76. doi:https://doi.org/10.1016/j.ememar.2018.11.005.
- Wang GJ, Xie C, Wen D, Zhao L. When Bitcoin meets Economic Policy Uncertainty (EPU): measuring risk spillover effect from EPU to Bitcoin. Finance Res Lett. 2019;31. doi:https://doi.org/10.1016/j.frl.2018.12.028.
- Kjærland F, Khazal A, Krogstad EA, Nordstrøm FB, Oust A. An analysis of bitcoin’s price dynamics. J Risk Financ Manage. 2018;11(4):63. doi:https://doi.org/10.3390/jrfm11040063.
- Kalyvas A, Papakyriakou P, Sakkas A, Urquhart A. What drives Bitcoin’s price crash risk? Econ Lett. 2020;191:108777. doi:https://doi.org/10.1016/j.econlet.2019.108777.
- Guesmi K, Saadi S, Abid I, Ftiti Z. Portfolio diversification with virtual currency: evidence from bitcoin. Int Rev Financ Anal. 2019;63:431–43. doi:https://doi.org/10.1016/j.irfa.2018.03.004.
- Al-Yahyaee KH, Rehman MU, Mensi W, Al-Jarrah IM. Can uncertainty indices predict Bitcoin prices? A revisited analysis using partial and multivariate wavelet approaches. North Am J Econom Finance. 2019;49:47–56. doi:https://doi.org/10.1016/j.najef.2019.03.019.
- Xiaolei S, Mingxi L, Zeqian S. A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Res Lett. 2020;32:1–6.
- Wolman D. Time for cash to cash out? Wall Street J. 2012 Feb 11 [accessed 2020 Oct 16]. https://www.wsj.com/articles/SB10001424052970204136404577209241595751130.
- Vigna P. Bitcoin couple travels the world using virtual cash. Wall Street J. 2013 Nov 20 [accessed 2020 Oct 16]. https://www.wsj.com/articles/SB10001424052702303789604579196171277465460.
- Curran R. Bitcoin fund linked to currency’s rally. Wall Street J. 2015 Dec 6 [accessed 2020 Oct 16]. https://www.wsj.com/articles/bitcoin-fund-may-have-contributed-to-currencys-rally-1449460380.
- Almudhaf F. Pricing efficiency of Bitcoin trusts. Appl Econ Lett. 2018;25(7):504–08. doi:https://doi.org/10.1080/13504851.2017.1340564.
- Kwok J. On prices and premiums of Bitcoin investment trust. Appl Econ Lett. 2020;27(16):1323–26. doi:https://doi.org/10.1080/13504851.2019.1678726.
- Vigna P. Why bitcoin is surging, again, up 130% this year. Wall Street Journal. 2017 May 22 [accessed 2020 Oct 16]. https://www.wsj.com/articles/why-bitcoin-is-surging-again-up-130-this-year-1495473654
- Mattke J, Maier C, Reis L, Weitzel T. Bitcoin Investment: a mixed methods study of investment motivations. Eur J Inf Syst. 2020;1–25. doi:https://doi.org/10.1080/0960085X.2020.1787109.
- ElBahrawy A, Alessandretti L, Kandler A, Pastor-Satorras R, Baronchelli A. Evolutionary dynamics of the cryptocurrency market. R Soc Open Sci. 2017;4(11):170623. doi:https://doi.org/10.1098/rsos.170623.
- Rogers E. Diffusion of Innovations. New York City, NY: Simon and Schuster; 2010.
- Pesaran M, Shin Y, Smith R. Bounds testing approaches to the analysis of level relationships. J Appl Econom. 2001;16(3):289–326. doi:https://doi.org/10.1002/jae.616.
- Glaser F, Zimmerman K, Haferkorn M, Weber M, Siering M. Bitcoin-asset or currency? Revealing users’ hidden intentions. Revealing Users’ Hidden Intentions (April 15, 2014). Tel Aviv, Israel: ECIS; 2014.
- Federal Reserve Bank of St. Louis. Federal Reserve Bank of St. Louis. Federal Reserve Bank of St. Louis; n.d. https://www.stlouisfed.org/.
- Perron P. The great crash, the oil price shock, and the unit root hypothesis. Econometrica. 1989;57(6):1361–401. doi:https://doi.org/10.2307/1913712.
- Zivot E, Andrews D. Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. J Bus Econ Stat. 1992;20(1):25–44. doi:https://doi.org/10.1198/073500102753410372.
- Perron P, Vogelsang T. Nonstationarity and level shifts with an application purchasing power parity. J Bus Econ Stat. 1992;10:301–20.
- Perron P. Further evidence on breaking trend functions in macroeconomic variables. J Econom. 1997;80(2):355–85. doi:https://doi.org/10.1016/S0304-4076(97)00049-3.
- Bai J. Estimating multiple breaks one at a time. Econom Theory. 1997;13(3):315–52. doi:https://doi.org/10.1017/S0266466600005831.
- Bai J. Estimation of a change point in multiple regression models. Rev Econom Stat. 1997;70(4):551–63. doi:https://doi.org/10.1162/003465397557132.
- Bai J, Perron P. Estimating and testing linear models with multiple structural changes. Econometrica. 1998;66(1):47–78. doi:https://doi.org/10.2307/2998540.
- Bai J, Perron P. Computation and analysis of multiple structural change models. J Appl Econom. 2003;18(1):1–22. doi:https://doi.org/10.1002/jae.659.
- Zeileis A, Leisch F, Hornik K, Kleiber C. Strucchange. an R package for testing for structural change in linear regression models. Wien (Austria): Vienna University of Economics and Business Administration; 2001.
- Zeileis A, Shah A, Patnaik I. Testing, monitoring, and dating structural changes in exchange rate regimes. Comput Stat Data Anal. 2010;54(6):1696–706. doi:https://doi.org/10.1016/j.csda.2009.12.005.
- Zeileis A, Kleiber C, Kramer W, Hornik K. Testing and dating of structural changes in practice. Comput Stat Data Anal. 2003;44(1–2):109–23. doi:https://doi.org/10.1016/S0167-9473(03)00030-6.
- Tully E, Lucey BM. A power GARCH examination of the gold market. Rese Int Bus Finance. 2007;21(2):316–25. doi:https://doi.org/10.1016/j.ribaf.2006.07.001.
- Agnolucci P. Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models. Energy Econ. 2009;31(2):316–21. doi:https://doi.org/10.1016/j.eneco.2008.11.001.
- Chiang TC, Doong SC. Empirical analysis of stock returns and volatility: evidence from seven Asian stock markets based on TAR-GARCH model. Rev Quant Finance Accounting. 2001;17(3):301–18. doi:https://doi.org/10.1023/A:1012296727217.
- Katsiampa P. Volatility estimation for Bitcoin: A comparison of GARCH models. Econ Lett. 2017;158:3–6. doi:https://doi.org/10.1016/j.econlet.2017.06.023.
- Chu J, Chan S, Nadarajah S, Osterrieder J. GARCH modelling of cryptocurrencies. J Risk Financ Manage. 2017;10(4):17. doi:https://doi.org/10.3390/jrfm10040017.
- Gyamerah SA. Modelling the volatility of Bitcoin returns using GARCH models. Quant Finance Econom. 2019;3(4):739. doi:https://doi.org/10.3934/QFE.2019.4.739.