Author
Listed:
- Ibrahim Yagli
(Nevsehir Haci Bektas Veli University, Faculty of Economics and Administrative Science, Business Administration Department)
- Ozkan Haykir
(Nigde Omer Halisdemir University, Faculty of Economics and Administrative Science, Finance and Banking Department)
Abstract
The market capitalization of cryptocurrencies has significantly increased despite their high volatility, and professionals and scholars are interested in discovering their price dynamics. In this study, we examine the determinants of large-price swings (more than 10%, 15%, and 20%), given that price changes in the cryptocurrency market are of a large magnitude. More specifically, we aim to determine whether past or market return has the power to predict subsequent daily large price movements (“jumps” or “dumps”) in cryptocurrency. The study also investigates the impact of co-explosivity, size, and uncertainty on large price movements. To do so, we employ daily price, market capitalization, and volume of the largest 1200 cryptocurrencies (excluding stable coins) based on their market capitalization. Given that large price movements are recurring phenomena in the cryptocurrency market, we adopt the Cox proportional hazards model. The empirical findings reveal that the likelihood of experiencing large price increases is higher if cryptocurrency returns are positive on the previous day. On the other hand, the risk of facing significant price increases (decreases) is lower (higher) when the market return is positive on the previous day. Our results also show that price increases in large magnitude experienced in the ten highest cryptocurrencies based on market capitalization do not have a significant impact on the large price increases. When we turn our attention to the size, we find that one-day lag market returns are mostly insignificant for large price increases, whereas they are rather positive and significant (except for small-cap groups) for large price drops. Regarding uncertainty, we ascertain that there are no significant changes between high and low uncertainty periods, especially for large price increases. Our results are robust in relation to the variables used in the analysis. Both in-sample and out-of-sample assessments affirm that our estimation models exhibit comparable predictive power.
Suggested Citation
Ibrahim Yagli & Ozkan Haykir, 2024.
"Understanding Drivers of Boom and Bust in Cryptocurrency Markets,"
Springer Proceedings in Business and Economics, in: Nesrin Ozatac & Nigar Taspinar & Bezhan Rustamov (ed.), Sustainable Development in Banking and Finance, pages 7-30,
Springer.
Handle:
RePEc:spr:prbchp:978-3-031-65533-3_2
DOI: 10.1007/978-3-031-65533-3_2
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