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In this paper, we analyze the time-series of minute price returns on theBitcoin market through the statistical models of generalized autoregressiveconditional heteroskedasticity (GARCH) family. Several mathematical models havebeen proposed in finance, to model the dynamics of price returns, each of themintroducing a different perspective on the problem, but none withoutshortcomings. We combine an approach that uses historical values of returns andtheir volatilities - GARCH family of models, with a so-called "Mixture ofDistribution Hypothesis", which states that the dynamics of price returns aregoverned by the information flow about the market. Using time-series ofBitcoin-related tweets and volume of transactions as external information, wetest for improvement in volatility prediction of several GARCH model variantson a minute level Bitcoin price time series. Statistical tests show that thesimplest GARCH(1,1) reacts the best to the addition of external signal to modelvolatility process on out-of-sample data.

Time-varying volatility in Bitcoin market and information flow at minute-level frequency