Abstract The primary purpose of this paper is to investigate whether a novel Markov regime-switching mixed-data sampling (MRS-MIADS) model we design can improve the prediction accuracy of the realized variance (RV) of Bitcoin. Moreover, to verify whether the importance of jumps for RV forecasting changes over time, we extend the standard MIDAS model to characterize two volatility regimes and introduce a jump-driven time-varying transition probability between the two regimes. Our results suggest that the proposed novel MRS-MIDAS model exhibits statistically significant improvement for forecasting the RV of Bitcoin. In addition, we find that jump occurrences significantly increase the persistence of the high-volatility regime and switch between high- and low-volatility regimes. A wide range of checks confirm the robustness of our results. Finally, the proposed model shows significant improvement for 2-week and 1-month horizon forecasts.
Description
Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach - Ma - 2020 - Journal of Forecasting - Wiley Online Library
%0 Journal Article
%1 https://doi.org/10.1002/for.2691
%A Ma, Feng
%A Liang, Chao
%A Ma, Yuanhui
%A Wahab, M.I.M.
%D 2020
%J Journal of Forecasting
%K ba crypto_curr volatility
%N 8
%P 1277-1290
%R https://doi.org/10.1002/for.2691
%T Cryptocurrency volatility forecasting: A Markov regime-switching MIDAS approach
%U https://onlinelibrary.wiley.com/doi/abs/10.1002/for.2691
%V 39
%X Abstract The primary purpose of this paper is to investigate whether a novel Markov regime-switching mixed-data sampling (MRS-MIADS) model we design can improve the prediction accuracy of the realized variance (RV) of Bitcoin. Moreover, to verify whether the importance of jumps for RV forecasting changes over time, we extend the standard MIDAS model to characterize two volatility regimes and introduce a jump-driven time-varying transition probability between the two regimes. Our results suggest that the proposed novel MRS-MIDAS model exhibits statistically significant improvement for forecasting the RV of Bitcoin. In addition, we find that jump occurrences significantly increase the persistence of the high-volatility regime and switch between high- and low-volatility regimes. A wide range of checks confirm the robustness of our results. Finally, the proposed model shows significant improvement for 2-week and 1-month horizon forecasts.
@article{https://doi.org/10.1002/for.2691,
abstract = {Abstract The primary purpose of this paper is to investigate whether a novel Markov regime-switching mixed-data sampling (MRS-MIADS) model we design can improve the prediction accuracy of the realized variance (RV) of Bitcoin. Moreover, to verify whether the importance of jumps for RV forecasting changes over time, we extend the standard MIDAS model to characterize two volatility regimes and introduce a jump-driven time-varying transition probability between the two regimes. Our results suggest that the proposed novel MRS-MIDAS model exhibits statistically significant improvement for forecasting the RV of Bitcoin. In addition, we find that jump occurrences significantly increase the persistence of the high-volatility regime and switch between high- and low-volatility regimes. A wide range of checks confirm the robustness of our results. Finally, the proposed model shows significant improvement for 2-week and 1-month horizon forecasts.},
added-at = {2021-12-20T16:03:15.000+0100},
author = {Ma, Feng and Liang, Chao and Ma, Yuanhui and Wahab, M.I.M.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/242fcf2299af2ff1bd83d3e543eec6267/georglender},
description = {Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach - Ma - 2020 - Journal of Forecasting - Wiley Online Library},
doi = {https://doi.org/10.1002/for.2691},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/for.2691},
interhash = {4cca591077a2aa8df3da1f1219187c5b},
intrahash = {42fcf2299af2ff1bd83d3e543eec6267},
journal = {Journal of Forecasting},
keywords = {ba crypto_curr volatility},
number = 8,
pages = {1277-1290},
timestamp = {2021-12-20T15:03:15.000+0100},
title = {Cryptocurrency volatility forecasting: A Markov regime-switching MIDAS approach},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/for.2691},
volume = 39,
year = 2020
}
