How to measure the liquidity of cryptocurrency markets?


Brauneis, Alexander ; Mestel, Roland ; Riordan, Ryan ; Theissen, Erik


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DOI: https://doi.org/10.1016/j.jbankfin.2020.106041
URL: https://www.sciencedirect.com/science/article/pii/...
URN: urn:nbn:de:bsz:180-madoc-621080
Document Type: Article
Year of publication Online: 2021
The title of a journal, publication series: Journal of Banking and Finance
Volume: 124
Issue number: Article 106041
Page range: 1-26
Place of publication: Amsterdam
Publishing house: Elsevier North-Holland
ISSN: 0378-4266
Publication language: English
Institution: Business School > ABWL u. Finanzierung (Theissen)
Pre-existing license: Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 330 Economics
Abstract: This paper investigates the efficacy of low-frequency transactions-based liquidity measures to describe actual (high-frequency) liquidity. We show that the Corwin and Schultz (2012) and Abdi and Ranaldo (2017) estimators outperform other measures in describing time-series variations, irrespective of the observation frequency, trading venue, high-frequency liquidity benchmark, and cryptocurrency. Both measures perform well during high and low return, volatility and volume periods. The Kyle and Obizhaeva (2016) estimator and the Amihud (2002) illiquidity ratio outperform when estimating liquidity levels. These two estimators also reliably identify liquidity differences between trading venues. Overall, the results suggest that there is not yet a universally bestmeasure but there are reasonably good low-frequency measures.
Additional information: Online-Ressource

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