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.
Highlights
Cryptocurrencies recently attracted massive attention from public and researcher community in several disciplines such as finance and economics
We propose the use of Temporal Mixture Ensemble (TME), presented below, and we benchmark its performance against two econometric baseline models (ARMAGARCH and ARMAX-generalized autoregressive conditional heteroskedasticity (GARCH)) and one Machine Learning baseline model (Gradient Boosting Machine)
The fact that GBM has superior performance on mean absolute error (MAE) but not root mean square error (RMSE) might be related to the fact that GBM has been trained to minimize point prediction, while autoregressive moving average model (ARMA)-GARCH, ARMAX-GARCH and TME were trained with a maximum likelihood objective, that handles better the larger deviations
Summary
Antulov-Fantulin et al.2016; Cheah and Fry 2015; Chu et al 2015; Donier and Bouchaud 2015; Ciaian et al 2016), computer science (Ron and Shamir 2013; Jang and Lee 2018; Amjad and Shah 2016; Alessandretti et al 2018; Guo et al 2018; Beck et al 2019) or complex systems (Garcia and Schweitzer 2015; Wheatley et al 2019; Gerlach et al 2019; AntulovFantulin et al 2018; Kondor et al 2014; ElBahrawy et al 2017) It originated from a decentralized peer-to-peer payment network (Nakamoto 2008), relying on cryptographic methods (Bos et al 2014; Mayer 2016) like elliptical curve cryptography and the SHA-256 hash function. Bitcoin is one of the most prominent decentralized digital cryptocurrencies and it is the focus of this paper, the model developed below can be adapted to other cryptocurrencies with ease, as well as to other “ordinary” assets (equities, futures, FX rates, etc.)
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