Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting

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.

Risk spillover between Bitcoin and conventional financial markets: An expectile-based approach

“…In fact, Kurka (2019) indicate that the increasing market value of Bitcoin reinforces the risk spillover from Bitcoin, leading to some disruptions to the financial system. It could be that the time-variation in the risk spillovers reflects not only exogenous shocks related to economic and financial factors (Walther et al, 2019;Matkovskyy et al, 2020) but also security issues (Conti et al, 2018) and bubble risks (Su et al, 2018), both of which might contribute to the significant spillover effects between Bitcoin and conventional assets.…”

“…Walther et al (2019) provide significant evidence on the role of some exogenous factors related to economic and financial variables in driving the volatility of Bitcoin.…”

The Butterfly “Affect”: impact of development practices on cryptocurrency prices

“…A large body of literature is also looking at the volatility of cryptocurrencies, from the model estimation point of view [11,12] as well as by extrapolating the mechanisms driving the fluctuations. Studies showed, for example, a strong correlation with global economic activity [13,14] and volume of trades [15].…”

A Model of the Optimal Selection of Crypto Assets

“…For example, Cocco et al [28] present a Bitcoin market with different types of traders (chartist and/or random), reproducing some key stylized facts of the market, such as Bitcoin price typical time series and volatility clustering. Several studies are also investigating the drivers of the volatility of major cryptocurrencies, showing a strong link between the volatility and the global economic activity [29,30] and a weak dependence on country-specific factors [30].…”