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

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  • Irena Barjav{s}i'c
  • Nino Antulov-Fantulin

Abstract

In this paper, we analyze the time-series of minute price returns on the Bitcoin market through the statistical models of generalized autoregressive conditional heteroskedasticity (GARCH) family. Several mathematical models have been proposed in finance, to model the dynamics of price returns, each of them introducing a different perspective on the problem, but none without shortcomings. We combine an approach that uses historical values of returns and their volatilities - GARCH family of models, with a so-called "Mixture of Distribution Hypothesis", which states that the dynamics of price returns are governed by the information flow about the market. Using time-series of Bitcoin-related tweets and volume of transactions as external information, we test for improvement in volatility prediction of several GARCH model variants on a minute level Bitcoin price time series. Statistical tests show that the simplest GARCH(1,1) reacts the best to the addition of external signal to model volatility process on out-of-sample data.

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  • Irena Barjav{s}i'c & Nino Antulov-Fantulin, 2020. "Time-varying volatility in Bitcoin market and information flow at minute-level frequency," Papers 2004.00550, arXiv.org, revised Jan 2021.
    • Handle: RePEc:arx:papers:2004.00550
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    Cited by:

    1. M. Eren Akbiyik & Mert Erkul & Killian Kaempf & Vaiva Vasiliauskaite & Nino Antulov-Fantulin, 2021. "Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data," Papers 2110.14317, arXiv.org, revised Dec 2022.

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