Exchange market liquidity prediction with the k-nearest neighbor approach: Crypto vs. fiat currencies

Klender Cortez, Martha Del Pilar Rodríguez-García, Samuel Mongrut

Producción científica: Contribución a una revistaArtículo de revista revisión exhaustiva

8 Citas (Scopus)


In this paper, we compare the predictions on the market liquidity in crypto and fiat currencies between two traditional time series methods, the autoregressive moving average (ARMA) and the generalized autoregressive conditional heteroskedasticity (GARCH), and the machine learning algorithm called the k-nearest neighbor (KNN) approach. We measure market liquidity as the log rates of bid-ask spreads in a sample of three cryptocurrencies (Bitcoin, Ethereum, and Ripple) and 16 major fiat currencies from 9 February 2018 to 8 February 2019. We find that the KNN approach is better suited for capturing the market liquidity in a cryptocurrency in the short-term than the ARMA and GARCH models maybe due to the complexity of the microstructure of the market. Considering traditional time series models, we find that ARMA models perform well when estimating the liquidity of fiat currencies in developed markets, whereas GARCH models do the same for fiat currencies in emerging markets. Nevertheless, our results show that the KNN approach can better predict the log rates of the bid-ask spreads of crypto and fiat currencies than ARMA and GARCH models.
Idioma originalInglés
Número de artículo56
Páginas (desde-hasta)1-15
Número de páginas15
EstadoPublicada - 1 ene. 2021

Nota bibliográfica

Funding Information:
Funding: This article was written as part of a research project titled “Impact of technology on finance: evolution and future of Fintech” (PAICYT, CSA1312-20), which was sponsored by the Universidad Autonoma de Nuevo León.

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.


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