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.

Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume

Antulov-Fantulin, Nino;Lillo, Fabrizio
2021

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.
2021
Settore SECS-S/06 - Metodi mat. dell'economia e Scienze Attuariali e Finanziarie
Cryptocurrency markets; Econometrics; Machine learning; Temporal mixture ensemble
   Horizon 2020
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/128236
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