We apply the GARCH-MIDAS framework to forecast the daily, weekly, and monthly volatility of five highly capitalized Cryptocurrencies (Bitcoin, Etherium, Litecoin, Ripple, and Stellar) as well as the Cryptocurrency index CRIX. Based on the prediction quality, we determine the most important exogenous drivers of volatility in Cryptocurrency markets. We find that the Global Real Economic Activity outperforms all other economic and financial drivers under investigation. We also show that the Global Real Economic Activity provides superior volatility predictions for both, bull and bear markets. In addition, the average forecast combination results in low loss functions. This indicates that the information content of exogenous factors is time-varying and the model averaging approach diversifies the impact of single drivers.
Description
Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting - ScienceDirect
%0 Journal Article
%1 WALTHER2019101133
%A Walther, Thomas
%A Klein, Tony
%A Bouri, Elie
%D 2019
%J Journal of International Financial Markets, Institutions and Money
%K ba crypto_curr volatility
%P 101133
%R https://doi.org/10.1016/j.intfin.2019.101133
%T Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting
%U https://www.sciencedirect.com/science/article/pii/S1042443119302446
%V 63
%X We apply the GARCH-MIDAS framework to forecast the daily, weekly, and monthly volatility of five highly capitalized Cryptocurrencies (Bitcoin, Etherium, Litecoin, Ripple, and Stellar) as well as the Cryptocurrency index CRIX. Based on the prediction quality, we determine the most important exogenous drivers of volatility in Cryptocurrency markets. We find that the Global Real Economic Activity outperforms all other economic and financial drivers under investigation. We also show that the Global Real Economic Activity provides superior volatility predictions for both, bull and bear markets. In addition, the average forecast combination results in low loss functions. This indicates that the information content of exogenous factors is time-varying and the model averaging approach diversifies the impact of single drivers.
@article{WALTHER2019101133,
abstract = {We apply the GARCH-MIDAS framework to forecast the daily, weekly, and monthly volatility of five highly capitalized Cryptocurrencies (Bitcoin, Etherium, Litecoin, Ripple, and Stellar) as well as the Cryptocurrency index CRIX. Based on the prediction quality, we determine the most important exogenous drivers of volatility in Cryptocurrency markets. We find that the Global Real Economic Activity outperforms all other economic and financial drivers under investigation. We also show that the Global Real Economic Activity provides superior volatility predictions for both, bull and bear markets. In addition, the average forecast combination results in low loss functions. This indicates that the information content of exogenous factors is time-varying and the model averaging approach diversifies the impact of single drivers.},
added-at = {2021-12-20T15:59:27.000+0100},
author = {Walther, Thomas and Klein, Tony and Bouri, Elie},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2bb1a98f4e859805b2e3d19db7a04727c/georglender},
description = {Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting - ScienceDirect},
doi = {https://doi.org/10.1016/j.intfin.2019.101133},
interhash = {57ede373347a8fd94c17e5bb8a60d66a},
intrahash = {bb1a98f4e859805b2e3d19db7a04727c},
issn = {1042-4431},
journal = {Journal of International Financial Markets, Institutions and Money},
keywords = {ba crypto_curr volatility},
pages = 101133,
timestamp = {2021-12-20T14:59:27.000+0100},
title = {Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting},
url = {https://www.sciencedirect.com/science/article/pii/S1042443119302446},
volume = 63,
year = 2019
}
