The properties of time series of liquidity measures in cryptocurrency markets

Radosław  Pietrzyk; Krzysztof Piontek; Paweł Rokita

doi:https://doi.org/10.59139/ws.2025.10.1

Radosław Pietrzyk Uniwersytet Ekonomiczny we Wrocławiu, Wydział Ekonomii i Finansów, Katedra Inwestycji Finansowych i Zarządzania Ryzykiem, Polska / Wroclaw University of Economics and Business, Faculty of Economics and Finance, Department of Financial Investments and Risk Management, Poland ORCID: https://orcid.org/0000-0002-6583-8424 , Krzysztof Piontek Uniwersytet Ekonomiczny we Wrocławiu, Wydział Ekonomii i Finansów, Katedra Inwestycji Finansowych i Zarządzania Ryzykiem, Polska / Wroclaw University of Economics and Business, Faculty of Economics and Finance, Department of Financial Investments and Risk Management, Poland ORCID: https://orcid.org/0000-0001-9197-561X , Paweł Rokita Uniwersytet Ekonomiczny we Wrocławiu, Wydział Ekonomii i Finansów, Katedra Inwestycji Finansowych i Zarządzania Ryzykiem; Empirica Sp. z o.o., Polska / Wroclaw University of Economics and Business, Faculty of Economics and Finance, Department of Financial Investments and Risk Management; Empirica Ltd., Poland ORCID: https://orcid.org/0000-0003-2467-5339 Wiadomości Statystyczne. The Polish Statistician, vol. 70, 2025, 10, s. 1-39 Opublikowano online: 31 października 2025 DOI https://doi.org/10.59139/ws.2025.10.1 Sposób cytowania: Pietrzyk, R., Piontek, K., Rokita, P. (2025). The properties of time series of liquidity measures in cryptocurrency markets. Wiadomości Statystyczne. The Polish Statistician, 70(10), 1–39. https://doi.org/10.59139/ws.2025.10.1.

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ARTYKUŁ

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STRESZCZENIE

The study described in this paper analyses the properties of time series of two market liquidity measures, spread quotient and bias, for three cryptocurrencies: Bitcoin (BTC), Litecoin (LTC), and XRP. The analysis is carried out on the basis of order book data from three cryptocurrency exchanges: Binance, Bitfinex, and Coinbase. The study aims to identify the time-series properties of selected liquidity measures to assess whether their characteristics remain consistent across different cryptocurrencies and exchanges, and whether measures representing two distinct dimensions of liquidity – market tightness (spread quotient) and market depth (bias) – behave similarly to each other or rather clearly differently. We examined time series obtained by averaging tick-level order-book data over 5-, 15-, and 60-minute intervals. The time series of the selected measures were characterised by distinct properties. In the bias series, the following characteristics were identified: an ARMA effect, a Power GARCH effect, a fat-tailed conditional distribution of residuals, symmetry of the residual distribution and the absence of leverage effects. The spread series, on the other hand, were characterised by mean reversion and a level-GARCH effect, i.e. the higher the spread (which is indicative of a market’s lower liquidity), the greater the conditional variance, and the News Impact Curve depends on the process level. Moreover, the level effect in the conditional variance model was more pronounced in hourly data than in 15-minute data, and by the same token, it was more pronounced in 15-minute data than in 5-minute data. The specific properties of the spread quotient series vary according to the token (cryptocurrency), the cryptocurrency exchange, and the aggregation frequency for which the study is performed, whereas the characteristics of the bias time series remain more consistent across these dimensions.

SŁOWA KLUCZOWE

market liquidity, liquidity measures, cryptocurrency, Bitcoin

JEL

C1, C4, G1

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