References
- Ajaz, T., and A. S. Kumar. 2018. Herding in crypto-currency markets. Annals of Financial Economics 13 (2):1850006. doi:https://doi.org/10.1142/S2010495218500069.
- Alvarez-Ramirez, J., E. Rodriguez, and C. Ibarra-Valdez. 2018. Long-range correlations and asymmetry in the Bitcoin market. Physica A: Statistical Mechanics and Its Applications 492:948–55. doi:https://doi.org/10.1016/j.physa.2017.11.025.
- Al-Yahyaee, K. H., W. Mensi, and S. M. Yoon. 2018. Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets. Finance Research Letters 27:228–34. doi:https://doi.org/10.1016/j.frl.2018.03.017.
- Ante, L. 2020. Bitcoin transactions, information asymmetry and trading volume. Quantitative Finance and Economics 4 (3):365–81. doi:https://doi.org/10.3934/QFE.2020017.
- Ardia, D., K. Bluteau, and M. Ruede. 2019. Regime changes in Bitcoin GARCH volatility dynamics. Finance Research Letters 29:266–71. doi:https://doi.org/10.1016/j.frl.2018.08.009.
- Auer, R., and S. Claessens 2018. Regulating cryptocurrencies: Assessing market reactions. BIS Quarterly Review September. Available at SSRN: https://ssrn.com/abstract=3288097.
- Balcilar, M., E. Bouri, R. Gupta, and D. Roubaud. 2017. Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Economic Modelling 64:74–81. doi:https://doi.org/10.1016/j.econmod.2017.03.019.
- Barillas, F., and K. Nimark. 2018. Speculation and the bond market: An empirical no-arbitrage framework. Management Science 65 (9):4179–203. doi:https://doi.org/10.1287/mnsc.2018.3027.
- Bariviera, A. F. 2017. The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters 161:1–4. doi:https://doi.org/10.1016/j.econlet.2017.09.013.
- Baur, D. G., K. Hong, and A. D. Lee. 2018. Bitcoin: Medium of exchange or speculative assets? Journal of International Financial Markets, Institutions and Money 54:177–89. doi:https://doi.org/10.1016/j.intfin.2017.12.004.
- Baur, D. G., and T. Dimpfl. 2018. Asymmetric volatility in cryptocurrencies. Economics Letters 173:148–51. doi:https://doi.org/10.1016/j.econlet.2018.10.008.
- Begušić, S., Z. Kostanjčar, H. E. Stanley, and B. Podobnik. 2018. Scaling properties of extreme price fluctuations in Bitcoin markets. Physica A-Statistical Mechanics and Its Applications 510:400–06. doi:https://doi.org/10.1016/j.physa.2018.06.131.
- Bernardi, M., and L. Catania. 2016. Comparison of value-at-risk models using the MCS approach. Computational Stattistics 31 (2):579–608. doi:https://doi.org/10.1007/s00180-016-0646-6.
- Blau, B. M. 2017. Price dynamics and speculative trading in Bitcoin. Research in International Business and Finance 41:493–99. doi:https://doi.org/10.1016/j.ribaf.2017.05.010.
- Bouoiyour, J., and R. Selmi. 2015. What does Bitcoin look like? Annals of Economics and Finance 16 (2):449–92. https://www.researchgate.net/publication/283676718.
- Bouri, E., N. Jalkh, P. Molnar, and D. Roubaud. 2017a. Bitcoin for energy commodities before and after the December 2013 crash: Diversifier, hedge or safe haven? Applied Economic 49 (50):5063–73. doi:https://doi.org/10.1080/00036846.2017.1299102.
- Bouri, E., R. Gupta, A. K. Tiwari, and D. Roubaud. 2017b. Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Finance Research Letters 23:87–95. doi:https://doi.org/10.1016/j.frl.2017.02.009.
- Bouri, E., R. Gupta, and D. Roubaud. 2019. Herding behaviour in cryptocurrencies. Finance Research Letters 29:216–21. doi:https://doi.org/10.1016/j.frl.2018.07.008.
- Chaim, P., and M. P. Laurini. 2018. Volatility and return jumps in Bitcoin. Economics Letters 173:158–63. doi:https://doi.org/10.1016/j.econlet.2018.10.011.
- Cheah, E. T., and J. Fry. 2015. Speculative bubbles in bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics Letters 130:32–36. doi:https://doi.org/10.1016/j.econlet.2015.02.029.
- Chen, Y. 2018. Blockchain tokens and the potential democratization of entrepreneurship and innovation. Business Horizons 61 (4):567–75. doi:https://doi.org/10.1016/j.bushor.2018.03.006.
- Chernozhukov, V., and L. Umantsev. 2001. Conditional value-at-risk: Aspects of modeling and estimation. Empirical Economics 26 (1):271–93. doi:https://doi.org/10.1007/s001810000062.
- Chiang, T. C. 2020. Economic policy uncertainty and stock returns—evidence from the Japanese market. Quantitative Finance and Economics 4 (3):430–58. doi:https://doi.org/10.3934/QFE.2020020.
- Ciaian, P., M. Rajcaniova, and D. A. Kancs. 2016. The economics of Bitcoin price formation. Applied Economics 48 (19):1799–815. doi:https://doi.org/10.1080/00036846.2015.1109038.
- Cobbinah, J., T. Zhongming, and A. H. Ntarmah. 2020. Banking competition and stability: Evidence from West Africa. National Accounting Review 2 (3):263–84. doi:https://doi.org/10.3934/NAR.2020015.
- Covrig, V., and L. L. Ng. 2004. Volume autocorrelation, information, and investor trading. Journal of Banking and Finance 28 (9):2155–74. doi:https://doi.org/10.1016/j.jbankfin.2003.08.005.
- da Silva Filho, A. C., N. D. Maganini, and E. F. de Almeida. 2018. Multifractal analysis of Bitcoin market. Physica A: Statistical Mechanics and Its Applications 512:954–67. doi:https://doi.org/10.1016/j.physa.2018.08.076.
- Davidson, S., P. De Filippi, and J. Potts. 2018. Blockchains and the economic institutions of capitalism. Journal of Institutional Economics 14 (4):639–58. doi:https://doi.org/10.1017/S1744137417000200.
- De Bondt, W. F., and R. H. Thaler. 1995. Financial decision-making in markets and firms: A behavioral perspective. Handbooks in Operations Research and Management Science 9:385–410. doi:https://doi.org/10.1016/S0927-0507(05)80057-X.
- Drakos, A. A., G. P. Kouretas, and L. Zarangas. 2015. Predicting conditional autoregressive value-at-risk for stock markets during tranquil and turbulent periods. Journal of Financial Risk Management 4 (3):168–86. doi:https://doi.org/10.4236/jfrm.2015.43014.
- ElBahrawy, A., L. Alessandretti, A. Kandler, R. Pastor-Satorras, and A. Baronchelli. 2017. Evolutionary dynamics of the cryptocurrency market. Royal Society Open Science 4 (11):170623. doi:https://doi.org/10.1098/rsos.170623.
- Engle, R. F., and S. Manganelli. 2004. CAViaR: Conditional autoregressive value at risk by regression quantiles. Journal of Business & Economic Statistics 22 (4):367–81. doi:https://doi.org/10.1198/073500104000000370.
- Feng, W. J., Y. M. Wang, and Z. J. Zhang. 2018. Informed trading in the Bitcoin market. Finance Research Letters 26:63–70. doi:https://doi.org/10.1016/j.frl.2017.11.009.
- Ferraty, F., and A. Quintela-Del-Río. 2016. Conditional VAR and expected shortfall: A new functional approach. Econometric Reviews 35 (2):263–92. doi:https://doi.org/10.1080/07474938.2013.807107.
- Frijns, B., and R. Zwinkels. 2020. Speculation in European sovereign debt markets. Journal of Economic Behavior and Organization 169:245–65. doi:https://doi.org/10.1016/j.jebo.2019.11.017.
- Gandal, N., J. T. Hamrick, T. Moore, and T. Oberman. 2018. Price manipulation in the Bitcoin ecosystem. Journal of Monetary Economics 95:86–96. doi:https://doi.org/10.1016/j.jmoneco.2017.12.004.
- Garcia, D., C. J. Tessone, P. Mavrodiev, and N. Perony. 2014. The digital traces of bubbles: Feedback cycles between socio-economic signals in the Bitcoin economy. Journal of the Royal Society Interface 11 (99):1–8. doi:https://doi.org/10.1098/rsif.2014.0623.
- Gerlach, R., Z. D. Lu, and H. Huang. 2013. Exponentially smoothing the skewed laplace distribution for value-at-risk forecasting. Journal of Forecasting 32 (6):534–50. doi:https://doi.org/10.1002/for.2255.
- Gkillas, K., and P. Katsiampa. 2018. An application of extreme value theory to cryptocurrencies. Economics Letters 164:109–11. doi:https://doi.org/10.1016/j.econlet.2018.01.020.
- Gunter, F. R. 2017. Corruption, costs, and family: Chinese capital flight, 1984–2014. China Economic Review 43:105–17. doi:https://doi.org/10.1016/j.chieco.2017.01.010.
- Hamilton, J. D. 1987. A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57 (2): 357–384. doi: https://doi.org/10.2307/1912559.
- Hayes, A. S. 2016. Cryptocurrency value formation: An empirical study leading to a cost of production model for valuing bitcoin. Telematics and Informatics 34 (7):1308–21. doi:https://doi.org/10.1016/j.tele.2016.05.005.
- He, Z., L. He, and F. Wen. 2019. Risk compensation and market returns: The role of investor sentiment in the stock market. Emerging Markets Finance and Trade 55 (3):704–18. doi:https://doi.org/10.1080/1540496X.2018.1460724.
- Jeon, J., and J. W. Taylor. 2013. Using CAViaR Models with Implied Volatility for Value-at-Risk Estimation. Journal of Forecasting 32 (1):62–74. doi:https://doi.org/10.1002/for.1251.
- Joëts, M. 2014. Energy price transmissions during extreme movements. Economic Modelling 40:392–99. doi:https://doi.org/10.1016/j.econmod.2013.11.023.
- Kahneman, D. 1973. Attention and effort. Vol. 1063. Englewood Cliffs, NJ: Prentice-Hall.
- Koenker, R., and G. Bassett. 1978. Regression quantiles. Econometrica 46 (1):33–50. doi:https://doi.org/10.2307/1913643.
- Kristoufek, L. 2013. Bitcoin meets Google trends and wikipedia: Quantifying the relationship between phenomena of the Internet era. Scientific Reports 3 (1):3415. doi:https://doi.org/10.1038/srep03415.
- Kristoufek, L. 2018. On Bitcoin markets (in) efficiency and its evolution. Physica A: Statistical Mechanics and Its Applications 503:257–62. doi:https://doi.org/10.1016/j.physa.2018.02.161.
- Lahmiri, S., S. Bekiros, and A. Salvi. 2018. Long-range memory, distributional variation and randomness of bitcoin volatility. Chaos, Solitons & Fractals 107:43–48. doi:https://doi.org/10.1016/j.chaos.2017.12.018.
- Lan, J., T. Lu, and Z. Y. Tu. 2016. Capital flight and Bitcoin regulation. International Review of Finance 16 (3):445–55. doi:https://doi.org/10.1111/irfi.12072.
- Laporta, A. G., L. Merlo, and L. Petrella. 2018. Selection of value at risk models for energy commodities. Energy Economics 74:628–43. doi:https://doi.org/10.1016/j.eneco.2018.07.009.
- Latunde, T., L. S. Akinola, and D. D. Dare. 2020. Analysis of capital asset pricing model on Deutsche bank energy commodity. Green Finance 2 (1):20–34. doi:https://doi.org/10.3934/GF.2020002.
- Li, Z. H., H. Dong, Z. H. Huang, and P. Failler. 2018. Asymmetric effects on risks of Virtual Financial Assets (VFAs) in different regimes: A case of Bitcoin. Quantitative Finance and Economics 2 (4):860–83. doi:https://doi.org/10.3934/QFE.2018.4.860.
- Li, Z. H., S. L. Chen, and S. L. Chen. 2017. Statistical measure of validity of financial resources allocation. EURASIA Journal of Mathematics, Science and Technology Education 13 (12):7731–41. doi:https://doi.org/10.12973/ejmste/77932.
- Llorente, G., ., R. Michaely, G. Saar, and J. Wang. 2002. Dynamic volume-return relation of individual stocks. Review of Financial Studies 15 (4):1005–47. doi:https://doi.org/10.1093/rfs/15.4.1005.
- Meng, X. C., and J. W. Taylor. 2018. An approximate long-memory range-based approach for value at risk estimation. International Journal of Forecasting 34 (3):377–88. doi:https://doi.org/10.1016/j.ijforecast.2017.11.007.
- Pabuçcu, H., S. Ongan, and A. Ongan. 2020. Forecasting the movements of Bitcoin prices: An application of machine learning algorithms. Quantitative Finance and Economics 4 (4):679–92. doi:https://doi.org/10.3934/QFE.2020031.
- Peng, L., and W. Xiong. 2006. Investor attention, overconfidence and category learning. Journal of Financial Economics 80 (3):563–602. doi:https://doi.org/10.1016/j.jfineco.2005.05.003.
- Pieters, G., and S. Vivanco. 2017. Financial regulations and price inconsistencies across Bitcoin markets. Information Economics and Policy 39:1–14. doi:https://doi.org/10.1016/j.infoecopol.2017.02.002.
- Qureshi, K. 2016. Value-at-risk: The effect of autoregression in a quantile process. arXiv preprint arXiv:1605.04940. https://arxiv.org/abs/1605.04940.
- Righi, M. B., and P. S. Ceretta. 2015. Forecasting value at risk and expected shortfall based on serial pair-copula constructions. Expert Systems with Applications 42 (17–18):6380–90. doi:https://doi.org/10.1016/j.eswa.2015.04.023.
- Romero, P. A., S. B. Muela, and C. L. Martin. 2013. A comprehensive review of value at risk methodologies. Documentos De Trabajo (711):1. https://weimarqueses/alojamiento/soniabenito/workingpaper.
- Rubia, A., and L. Sanchis-Marco. 2013. On downside risk predictability through liquidity and trading activity: A dynamic quantile approach. International Journal of Forecasting 29:202–19. doi:https://doi.org/10.1016/j.ijforecast.2012.09.001.
- Saculsan, P. G., and T. Kanamura. 2020. Examining risk and return profiles of renewable energy investment in developing countries: The case of the Philippines. Green Finance 2 (2):135–50. doi:https://doi.org/10.3934/GF.2020008.
- Selmi, S., A. K. Tiwari, and S. Hammoudeh. 2018. Efficiency or speculation? A dynamic analysis of the Bitcoin market. Economics Bulletin 38 (4):2037–46.
- Sornette, D., P. Cauwels, and G. Smilyanov. 2018. Can we use volatility to diagnose financial bubbles? Lessons from 40 historical bubbles. Quantitative Finance and Economic 2 (1):1–105. doi:https://doi.org/10.3934/QFE.2018.1.1.
- Takaishi, T. 2018. Statistical properties and multifractality of Bitcoin. Physica A: Statistical Mechanics and Its Applications 506:507–19. doi:https://doi.org/10.1016/j.physa.2018.04.046.
- Urquhart, A. 2017. Price clustering in Bitcoin. Economics Letters 159:145–48. doi:https://doi.org/10.1016/j.econlet.2017.07.035.
- Vidal-Tomás, D., and A. Ibañez. 2018. Semi-strong efficiency of Bitcoin. Finance Research Letters 27:259–65. doi:https://doi.org/10.1016/j.frl.2018.03.013.
- Wang, G. J., C. Xie, D. Y. Wen, and L. F. Zhao. 2019. When Bitcoin meets economic policy uncertainty (EPU): Measuring risk spillover effect from EPU to Bitcoin. Finance Research Letters 31:489–97. doi:https://doi.org/10.1016/j.frl.2018.12.028.
- Wen, F., L. Xu, G. Ouyang, and G. Kou. 2019. Retail investor attention and stock price crash risk: Evidence from China. International Review of Financial Analysis 65:101376. doi:https://doi.org/10.1016/j.irfa.2019.101376.
- Whitea, H., T. H. Kimb, and S. Manganelli. 2015. VAR for VaR: Measuring tail dependence using multivariate regression quantiles. Journal of Econometrics 187 (1):169–88. doi:https://doi.org/10.1016/j.jeconom.2015.02.004.
- Yermack, D. 2013. Is Bitcoin A real currency? An economic appraisal, Working paper. National Bureau of Economic Research. Available from: http://www.nber.org/papers/w19747.
- Zhang, W., P. Wang, X. Li, and D. Shen. 2018. Multifractal detrended cross-correlation analysis of the return-volume relationship of Bitcoin market. Complexity 2018:8691420. doi:https://doi.org/10.1155/2018/8691420.