Exogenous Drivers of Cryptocurrency Volatility - A Mixed Data Sampling Approach to Forecasting

13 Pages Posted: 27 Jun 2018 Last revised: 11 Dec 2018

See all articles by Thomas Walther

Thomas Walther

University of St. Gallen - School of Finance

Tony Klein

Queen's University Belfast - Queen's Management School

Date Written: June 7, 2018

Abstract

We apply the GARCH-MIDAS framework to forecast the daily, weekly, and monthly volatility of four highly capitalized Cryptocurrencies (Bitcoin, Etherium, Litecoin, and Ripple) 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. Only the average forecast combination results in lower 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

JEL Classification: C10, C58, G11

Suggested Citation

Walther, Thomas and Klein, Tony, Exogenous Drivers of Cryptocurrency Volatility - A Mixed Data Sampling Approach to Forecasting (June 7, 2018). University of St.Gallen, School of Finance Research Paper No. 2018/19. Available at SSRN: https://ssrn.com/abstract=3192474 or http://dx.doi.org/10.2139/ssrn.3192474

Thomas Walther (Contact Author)

University of St. Gallen - School of Finance ( email )

Unterer Graben 21
St.Gallen, CH-9000
Switzerland

Tony Klein

Queen's University Belfast - Queen's Management School ( email )

Riddel Hall
185 Stranmillis Road
Belfast, BT9 5EE
United Kingdom

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