Liquidity and Predictability of Cryptoassets

• Název práce v češtině: Likvidita a prediktabilita kryptoaktiv
• Název v anglickém jazyce: Liquidity and Predictability of Cryptoassets
• Klíčová slova anglicky: Cryptoassets, Predictability, Liquidity, Panel data
• Akademický rok vypsání: 2018/2019
• Typ práce: diplomová práce
• Jazyk práce: angličtina
• Ústav: Institut ekonomických studií (23-IES)
• Vedoucí / školitel: prof. PhDr. Ladislav Krištoufek, Ph.D.
• Řešitel: skrytý - zadáno vedoucím/školitelem
• Datum přihlášení: 09.05.2019
• Datum zadání: 09.05.2019
• Datum a čas obhajoby: 03.02.2021 09:00
• Datum odevzdání elektronické podoby: 25.09.2020
• Datum proběhlé obhajoby: 03.02.2021
• Oponenti: PhDr. František Čech, Ph.D.

Seznam odborné literatury

Alvarez-Ramirez, J., Rodriguez, E., & Ibarra-Valdez, C. (2018). Long-range correlations and asymmetry in the Bitcoin market. Physica A: Statistical Mechanics and its Applications, 492, 948–955.

Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets, 5(1), 31–56.

Badev, A., & Chen, M. (2014). Bitcoin: Technical Background and Data Analysis (Finance and Economics Discussion Series No. 2014-104). Washington, D.C.: Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board.

Brauneis, A., & Mestel, R. (2018). Price discovery of cryptocurrencies: Bitcoin and beyond. Economics Letters, 165, 58–61.

Caporale, G. M., Gil-Alana, L., & Plastun, A. (2018). Persistence in the cryptocurrency market. Research in International Business and Finance, 46, 141–148.

Corwin, S. A., & Schultz, P. (2012). A Simple Way to Estimate Bid-Ask Spreads from Daily High and Low Prices. The Journal of Finance, 67(2), 719–759.

European Central Bank Cryptoassets Task Force. (2019). Cryptoassets: Implications for financial stability, monetary policy, and payments and market infrastructures (European Central Bank Occasional Paper Series No. 223). Frankfurt am Main: European Central Bank.

Goyenko, R. Y., Holden, C. W., & Trzcinka, C. A. (2009). Do liquidity measures measure liquidity? Journal of Financial Economics, 92(2), 153–181.

Krištoufek, L. (2018). On Bitcoin markets (in)efficiency and its evolution. Physica A: Statistical Mechanics and its Applications, 503, 257–262.

Schilling, L., & Uhlig, H. (2019). Some simple bitcoin economics. Journal of Monetary Economics, 106, 16–26.

Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80–82.

Wei, W. C. (2018). Liquidity and market efficiency in cryptocurrencies. Economics Letters, 168, 21–24.

Předběžná náplň práce v anglickém jazyce

Cryptoassets are an exciting and interesting asset class for potential investment. It may thus be important to analyze the relationship between cryptoasset liquidity and return predictability. These findings may then be used to better understand crypto-markets and perhaps create more befitting trading strategies. To the best of our knowledge only a few studies investigate the relationship between cryptoasset liquidity and return predictability. Two notable examples are Brauneis and Mestel (2018) as well as Wei (2018). Nevertheless, at the time of writing, no existing study examines this relation using panel data regressions. We thus analyze the relationship between cryptoasset liquidity and return predictability using both cross-sectional as well as panel data models.

Cryptoasset market data is obtained from CoinMarketCap. Following previous literature, namely Brauneis and Mestel (2018), we calculate various predictability measures. We specifically include the Bartels test, runs test, difference sign test, Ljung-Box test, turning point test, two Lo-MacKinlay variance ratio tests for different holding periods and the Geweke and Porter-Hudak (GPH) estimate. Four dependent variables are created based on the p-values from the seven tests and the absolute value of the GPH estimate. The variables are average cryptoasset rank, average p-value, the total number of tests that cannot reject the null hypothesis and a binary variable, equal to 1, when a cryptoasset has at least four tests for which the null hypothesis cannot be rejected. Liquidity is measured by the Amihud illiquidity ratio and the Corwin-Schultz spread estimate. Following the regression specification by Brauneis and Mestel (2018), other independent variables include Garman-Klass volatility, logarithm of volume, logarithm of market capitalization and the turnover ratio. To quantify the relationship between liquidity and predictability we regress the dependent variables on the independent variables using ordinary least squares and the logit for cross-sectional data as well as the fixed effects linear and logistic models for panel data.