Forecasting of BTC volatility: comparative study between parametric and nonparametric models
Author:
Funder
CNRST
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
http://link.springer.com/content/pdf/10.1007/s13748-019-00196-w.pdf
Reference86 articles.
1. Pedro, F.: Understanding Bitcoin: Cryptography. Engineering and Economics. Wiley, New York (2014). ISBN 978-1-119-01916-9
2. Werner, K., Marcel, C.M.: A hybrid volatility forecasting framework integrating GARCH. Artif. Neural Netw. Tech. Anal. Princ. Compon. Anal. Expert Syst. Appl. 109, 1–11 (2018). https://doi.org/10.1016/j.eswa.2018.05.011
3. Khaldi, R., El Afia, A., Chiheb, R., Faizi, R.: Forecasting of Bitcoin daily returns with EEMD-ELMAN based model. In: Proceedings of ACM LOPAL Conference, Rabat, Morocco, May 2018 (LOPAL’18) (2018). https://doi.org/10.1145/3230905.3230948
4. Urquhart, A.: The inefficiency of Bitcoin. Econ. Lett. 148, 80–82 (2016). https://doi.org/10.1016/j.econlet.2016.09.019
5. Yu, M., Gao, R., Su, X., Jin, X., Zhang, H., Song, J.: Forecasting Bitcoin volatility: the role of leverage effect and uncertainty. Phys. A. (2019). https://doi.org/10.1016/j.physa.2019.03.072
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