Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume

Nino Antulov-Fantulin, Tian Guo and Fabrizio Lillo ()
Additional contact information
Nino Antulov-Fantulin: ETH Zürich
Tian Guo: RAM Active Investments
Fabrizio Lillo: University of Bologna and Scuola Normale Superiore

Decisions in Economics and Finance, 2021, vol. 44, issue 2, No 19, 905-940

Abstract: Abstract We study the problem of the intraday short-term volume forecasting in cryptocurrency multi-markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble, capable of adaptively exploiting, for the forecasting, different sources of data and providing a volume point estimate, as well as its uncertainty. We provide evidence of the clear outperformance of our model with respect to econometric models. Moreover our model performs slightly better than Gradient Boosting Machine while having a much clearer interpretability of the results. Finally, we show that the above results are robust also when restricting the prediction analysis to each volume quartile.

Keywords: Econometrics; Machine learning; Cryptocurrency markets; Temporal mixture ensemble; C53; C58; G12 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10203-021-00344-9

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