Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?
Author:
Publisher
Springer Science and Business Media LLC
Subject
Management Science and Operations Research,General Decision Sciences
Link
https://link.springer.com/content/pdf/10.1007/s10479-021-04116-x.pdf
Reference58 articles.
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2. Andersen, T. G., Bollerslev, T., & Diebold, F. X. (2007). Roughing it up: Including jump components in the measurement, modeling, and forecasting of return volatility. The Review of Economics and Statistics, 89(4), 701–720. https://doi.org/10.1162/rest.89.4.701
3. Adcock, R., & Gradojevic, N. (2019). Non-fundamental, non-parametric Bitcoin forecasting. Physica A: Statistical Mechanics and Its Applications, 531, 121727. https://doi.org/10.1016/j.physa.2019.121727
4. Akyildirim, E., Corbet, S., Katsiampa, P., Kellard, N., & Sensoy, A. (2020). The development of bitcoin futures: Exploring the interactions between cryptocurrency derivatives. Finance Research Letters, 34, 101234. https://doi.org/10.1016/j.frl.2019.07.007
5. Akyildirim, E., Goncu, A., & Sensoy, A. (2021). Prediction of cryptocurrency returns using machine learning. Annals of Operations Research, 297(1), 3–36. https://doi.org/10.1007/s10479-020-03575-y
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