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Exchange Market Liquidity Prediction with the K-Nearest Neighbor Approach: Crypto vs. Fiat Currencies

Author

Listed:
  • Klender Cortez

    (Universidad Autonoma de Nuevo Leon, Facultad de Contaduria Publica y Administracion, San Nicolás de los Garza 66451, Mexico)

  • Martha del Pilar Rodríguez-García

    (Universidad Autonoma de Nuevo Leon, Facultad de Contaduria Publica y Administracion, San Nicolás de los Garza 66451, Mexico)

  • Samuel Mongrut

    (Tecnologico de Monterrey, EGADE Business School, San Pedro Garza García 66269, Mexico
    Universidad del Pacífico, Departamento de Finanzas, Jesús María 15072, Peru)

Abstract

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.

Suggested Citation

  • Klender Cortez & Martha del Pilar Rodríguez-García & Samuel Mongrut, 2020. "Exchange Market Liquidity Prediction with the K-Nearest Neighbor Approach: Crypto vs. Fiat Currencies," Mathematics, MDPI, vol. 9(1), pages 1-15, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2020:i:1:p:56-:d:470101
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