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

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

Research output: Contribution to journalArticle in a journalpeer-review

8 Scopus citations


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.

Original languageEnglish
Article number56
Pages (from-to)1-15
Number of pages15
Issue number1
StatePublished - 1 Jan 2021

Bibliographical note

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


  • Bitcoin
  • Digital money
  • Ethereum
  • Investor behavior
  • Ripple
  • Time series analysis
  • time series analysis
  • investor behavior
  • digital money


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