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Title Is There Any Witching in the Cryptocurrency Market?
Authors Plastun, Oleksii Leonidovych  
Khomutenko, Liudmyla Ivanivna  
Bashlai, S.
ORCID http://orcid.org/0000-0001-8208-7135
http://orcid.org/0000-0002-9443-4330
Keywords expiration day
witching day
abnormal returns;
cryptocurrency market
Type Article
Date of Issue 2022
URI https://essuir.sumdu.edu.ua/handle/123456789/87840
Publisher MDPI
License Creative Commons Attribution 4.0 International License
Citation Plastun, A.; Khomutenko, L.; Bashlai, S. Is There Any Witching in the Cryptocurrency Market? J. Risk Financial Manag. 2022, 15, 92. https://doi.org/10.3390/jrfm15020092
Abstract This paper explores price effects caused by the expiration of derivatives in the cryptocurrency market. Applying different statistical tests (ANOVA, Mann–Whitney, and t-tests) and econometric methods (the modified cumulative abnormal return approach, regression analysis with dummy variables, and the trading simulation approach) to daily and weekly Bitcoin data over the period 2018–2021, the following hypotheses are tested: (H1) Expiration days create patterns in price behavior in the cryptocurrency market; and (H2) Price patterns can be exploited to generate abnormal profits from trading. The results suggest that expiration effects are only nominally present in the cryptocurrency market. There are differences in returns between expiration-related periods and average returns, but these differences are statistically insignificant. The only case in which an anomaly was detected was related to abnormally high returns during the week of expiration: returns during such weeks were positive in 65% of cases, and were on average 5 times higher than during usual weeks. Trading strategies based on this fact were able to generate results different from those of random trading, with a Sharpe ratio above 1. This is evidence in favor of the existence of a real price anomaly, which contradicts the efficient market hypothesis, and this could be implemented in the practice of traders and investors by creating trading strategies based on detected price effects or special technical analysis indicators to generate trading signals. For academics, these results might provide an opportunity to improve time series forecasting analysis in the case of Bitcoin.
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