|
Aalborg, H. A., Molnár, P., and de Vries, J. E. (2019). What can explain the price, volatility and trading volume of bitcoin? Finance Research Letters, 29:255–265. Aharon, D. Y., Umar, Z., and Vo, X. V. (2021). Dynamic spillovers between the term structure of interest rates, bitcoin, and safe-haven currencies. Financial Innovation, 7(1):1–25. Alessandretti, L., ElBahrawy, A., Aiello, L. M., and Baronchelli, A. (2018). Anticipating cryptocurrency prices using machine learning. Complexity, 2018. Aras, S. (2021). Stacking hybrid garch models for forecasting bitcoin volatility. Expert Systems with Applications, 174:114747. Awartani, B. M. and Corradi, V. (2005). Predicting the volatility of the s&p-500 stock index via garch models: the role of asymmetries. International Journal of forecasting, 21(1):167–183. Baur, D. G. and Dimpfl, T. (2021). The volatility of bitcoin and its role as a medium of exchange and a store of value. Empirical Economics, 61(5):2663–2683. Bouoiyour, J., Selmi, R., and Wohar, M. E. (2019). Safe havens in the face of presidential election uncertainty: A comparison between bitcoin, oil and precious metals. Applied Economics, 51(57):6076–6088. Bouri, E., Gkillas, K., Gupta, R., and Pierdzioch, C. (2021). Forecasting realized volatility of bitcoin: The role of the trade war. Computational Economics, 57(1):29–53. Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32. Chen, W., Xu, H., Jia, L., and Gao, Y. (2021). Machine learning model for bitcoin exchange rate prediction using economic and technology determinants. International Journal of Forecasting, 37(1):28–43. Conlon, T. and McGee, R. (2020). Safe haven or risky hazard? bitcoin during the covid-19 bear market. Finance Research Letters, 35:101607. Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2):174–196. Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar–a garch volatility analysis. Finance Research Letters, 16:85–92. Elsayed, A. H., Gozgor, G., and Lau, C. K. M. (2022). Causality and dynamic spillovers among cryptocurrencies and currency markets. International Journal of Finance & Economics, 27(2):2026–2040. Fang, T., Su, Z., and Yin, L. (2020). Economic fundamentals or investor perceptions? the role of uncertainty in predicting long-term cryptocurrency volatility. International Review of Financial Analysis, 71:101566. Franses, P. H. and Van Dijk, D. (1996). Forecasting stock market volatility using (non- linear) garch models. Journal of forecasting, 15(3):229–235. Garcia-Jorcano, L. and Benito, S. (2020). Studying the properties of the bitcoin as a diversifying and hedging asset through a copula analysis: Constant and time-varying. Research in International Business and Finance, 54:101300. Görgen, K., Meirer, J., and Schienle, M. (2022). Predicting value at risk for cryptocurren- cies using generalized random forests. arXiv preprint arXiv:2203.08224. Gradojevic, N., Kukolj, D., Adcock, R., and Djakovic, V. (2021). Forecasting bitcoin with technical analysis: A not-so-random forest? International Journal of Forecasting. Huang, Y., Duan, K., and Mishra, T. (2021). Is bitcoin really more than a diversifier? a pre-and post-covid-19 analysis. Finance Research Letters, 43:102016. Huynh, T. L. D., Burggraf, T., and Wang, M. (2020). Gold, platinum, and expected bitcoin returns. Journal of Multinational Financial Management, 56:100628. Jaquart, P., Dann, D., and Weinhardt, C. (2021). Short-term bitcoin market prediction via machine learning. The journal of finance and data science, 7:45–66. Katsiampa, P. (2017). Volatility estimation for bitcoin: A comparison of garch models. Economics Letters, 158:3–6. Köchling, G., Schmidtke, P., and Posch, P. N. (2020). Volatility forecasting accuracy for bitcoin. Economics Letters, 191:108836. Kristjanpoller, W. and Minutolo, M. C. (2016). Forecasting volatility of oil price using an artificial neural network-garch model. Expert Systems with Applications, 65:233–241. Liang, C., Zhang, Y., Li, X., and Ma, F. (2022). Which predictor is more predictive for bit- coin volatility? and why? International Journal of Finance & Economics, 27(2):1947– 1961. López-Cabarcos, M. Á., Pérez-Pico, A. M., Piñeiro-Chousa, J., and Šević, A. (2021). Bitcoin volatility, stock market and investor sentiment. are they connected? Finance Research Letters, 38:101399. Luong, C. and Dokuchaev, N. (2018). Forecasting of realised volatility with the random forests algorithm. Journal of Risk and Financial Management, 11(4):61. Malladi, R. K. and Dheeriya, P. L. (2021). Time series analysis of cryptocurrency returns and volatilities. Journal of Economics and Finance, 45(1):75–94. Mensi, W., Sensoy, A., Aslan, A., and Kang, S. H. (2019). High-frequency asymmetric volatility connectedness between bitcoin and major precious metals markets. The North American Journal of Economics and Finance, 50:101031. Milunovich, G. and Lee, S. A. (2021). Cryptocurrency exchanges: Predicting which mar- kets will remain active. Journal of Forecasting. Moussa, W., Mgadmi, N., Béjaoui, A., and Regaieg, R. (2021). Exploring the dynamic relationship between bitcoin and commodities: New insights through stecm model. Re- sources Policy, 74:102416. Naimy, V., Haddad, O., Fernández-Avilés, G., and El Khoury, R. (2021). The predictive capacity of garch-type models in measuring the volatility of crypto and world curren- cies. PloS one, 16(1):e0245904. Nti, K. O., Adekoya, A., and Weyori, B. (2019). Random forest based feature selection of macroeconomic variables for stock market prediction. American Journal of Applied Sciences, 16(7):200–212. Pabuçcu, H., Ongan, S., and Ongan, A. (2020). Forecasting the movements of bitcoin prices: an application of machine learning algorithms. Quantitative Finance and Eco- nomics, 4(4):679–692. Qiu, Y., Wang, Z., Xie, T., and Zhang, X. (2021). Forecasting bitcoin realized volatility by exploiting measurement error under model uncertainty. Journal of Empirical Finance, 62:179–201. Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., and Chica-Rivas, M. (2015). Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71:804–818. Su, F., Wang, X., and Yuan, Y. (2022). The intraday dynamics and intraday price discovery of bitcoin. Research in International Business and Finance, 60:101625. Tan, C.-Y., Koh, Y.-B., Ng, K.-H., and Ng, K.-H. (2021). Dynamic volatility modelling of bitcoin using time-varying transition probability markov-switching garch model. The North American Journal of Economics and Finance, 56:101377. Tiwari, A. K., Kumar, S., and Pathak, R. (2019). Modelling the dynamics of bitcoin and litecoin: Garch versus stochastic volatility models. Applied Economics, 51(37):4073– 4082. Trucíos, C. (2019). Forecasting bitcoin risk measures: A robust approach. International Journal of Forecasting, 35(3):836–847. Urquhart, A. and Zhang, H. (2019). Is bitcoin a hedge or safe haven for currencies? an intraday analysis. International Review of Financial Analysis, 63:49–57. Wakefield, K. (2019). A guide to machine learning algorithms and their applications. undated, SAS. com,< https://www. sas. com/en_gb/insights/articles/analytics/machine- learning-algorithms. html. Walther, T., Klein, T., and Bouri, E. (2019). Exogenous drivers of bitcoin and cryptocur- rency volatility–a mixed data sampling approach to forecasting. Journal of Interna- tional Financial Markets, Institutions and Money, 63:101133. |