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

Klender Cortez, Martha del Pilar Rodríguez-García and Samuel Mongrut
Additional contact information
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

Mathematics, 2020, vol. 9, issue 1, 1-15

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.

Keywords: Bitcoin; digital money; Ethereum; investor behavior; Ripple; time series analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/1/56/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/1/56/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2020:i:1:p:56-:d:470101

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().