Abstract
As cryptocurrencies emerged only recently, they are subject to only very limited financial regulations. In this paper we study which variables can predict bubbles in the prices of eight major cryptocurrencies, focusing on uncertainty measures as predictors. We detect multiple bubble periods for all eight cryptocurrencies, particularly in 2017 and early 2018. We find that higher volatility, trading volume and transactions are positively associated with the presence of bubbles across cryptocurrencies. Regarding the uncertainty variables, the VIX-index consistently demonstrates negative relationships with bubble occurrence, while the EPU-index mostly exhibits positive associations with bubbles. These results may assist authorities in designing appropriate regulations.
Highlights
Emergence of cryptocurrencies was one of the most remarkable financial innovations of the last decade
We study whether any of four variables related to the particular cryptocurrency (Google search queries for the cryptocurrency’s name, its price volatility, number of transactions, trading volume) or three variables capturing uncertainty in general financial markets (the economic policy uncertainty (EPU) index, the VIX-index and the TED-spread) can predict those bubbles
We have examined whether certain variables can predict bubbles in cryptocurrency prices
Summary
Emergence of cryptocurrencies was one of the most remarkable financial innovations of the last decade. We study whether any of four variables related to the particular cryptocurrency (Google search queries for the cryptocurrency’s name, its price volatility, number of transactions, trading volume) or three variables capturing uncertainty in general financial markets (the economic policy uncertainty (EPU) index, the VIX-index and the TED-spread) can predict those bubbles. We employ search volume from Google trends in our analysis because it measures public interest in each specific cryptocurrency This variable is constructed as the relative level of web searches provided by Google, and has previously been demonstrated to have predictive potential, as Choi and Varian (2009), Choi and Varian (2012), Bijl et al (2016), Molnár and Bašta (2017) have reported. This indicates that collinearity is not a problem and that the variables capture different aspects or forms of uncertainty
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