Exogenous Drivers of Bitcoin and Cryptocurrency Volatility – A Mixed Data Sampling Approach to Forecasting

Thomas Walther, Tony Klein and Elie Bouri

No 2018/02, QBS Working Paper Series from Queen's University Belfast, Queen's Business School

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.

Keywords: Bitcoin; Cryptocurrencies; GARCH; Mixed Data Sampling; Volatility (search for similar items in EconPapers)
JEL-codes: C10 C58 G11 (search for similar items in EconPapers)
Date: 2018
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https://www.econstor.eu/bitstream/10419/271218/1/qms-rp2018-02.pdf (application/pdf)

Related works:
Journal Article: Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:qmsrps:201802

DOI: 10.2139/ssrn.3192474

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