Master Thesis About Cryptocurrency

Master thesis about cryptocurrency


In this paper we study variables that can predict bubbles in cryptocurrency prices.

Master thesis about cryptocurrency

Bubble periods are detected by employing a recursive augmented Dickey-Fuller algorithm called the PSY test, developed by \citet{phillips2015testing-a,phillips2015testing-b}. Through probit and linear regression models we study the possible predictors of the bubble periods.

Master thesis about cryptocurrency

We utilize both detected days and the underlying test statistics produced by the algorithm as dependent variables in the analysis. Compared to other studies, we emphasize uncertainty measures as predictors and include an extended selection of cryptocurrencies.

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We apply panel regressions to investigate predictors across cryptocurrencies and time series regressions to study predictors for specific cryptocurrencies. We detect multiple bubble periods in all cryptocurrencies, particularly in 2017 and early 2018. The predictive ability of the variables appear to be depedent on the cryptocurrency studied.

Master thesis about cryptocurrency

Though in general, we find that higher volatility and trading volume is positively associated with the presence of bubbles across cryptocurrencies. When it comes to uncertainty variables, the VIX-index consistently demonstrates negative relationships with bubble behavior.

Furthermore, transactions and the EPU-index mostly exhibit positive associations with bubbles, but the effects are dependent on the cryptocurrency examined.

Master thesis about cryptocurrency

In terms of bubble prediction, the probit models perform better than the linear models.


Master's thesis in Applied Finance

University of Stavanger, Norway