Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling
Journal article
Cerqueti, R, Giacalone, M and Mattera, R (2020). Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling. Information Sciences. 527, pp. 1-26. https://doi.org/10.1016/j.ins.2020.03.075
Authors | Cerqueti, R, Giacalone, M and Mattera, R |
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Abstract | Recently, cryptocurrencies have attracted a growing interest from investors, practitioners and researchers. Nevertheless, few studies have focused on the predictability of them. In this paper we propose a new and comprehensive |
Keywords | Generalized Error Distribution; GARCH models; Skewed distributions; volatility forecasting; non linear GARCH |
Year | 2020 |
Journal | Information Sciences |
Journal citation | 527, pp. 1-26 |
Publisher | Elsevier |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.ins.2020.03.075 |
Publication dates | |
03 Apr 2020 | |
Publication process dates | |
Accepted | 23 Mar 2020 |
Deposited | 01 Apr 2020 |
Accepted author manuscript | License File Access Level Open |
https://openresearch.lsbu.ac.uk/item/896vq
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Accepted author manuscript
InformationSciences_R3 submitted.pdf | ||
License: CC BY-NC-ND 4.0 | ||
File access level: Open |
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