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Lead-lag and Volatility Point Change Estimations for Cryptocurrencies

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Mutale J., Angaman E. S. E. F., Mba J. C.
Journal of Banking and Financial Economics
|
2024
|
issue 1(21)
54-76
EN
This study investigates the lead-lag relationships and volatility dynamics among four major cryptocurrencies – Bitcoin, Ethereum, Solana, and Polygon – during the turbulent year of 2022. We address three primary research questions: (1) To what extent do lead-lag relationships exist among major cryptocurrencies, and how do they challenge or support the notion of market efficiency in the crypto space? (2) How do volatility change points in different cryptocurrencies relate to each other and to major market events? (3) How can the identification of lead‑lag relationships and volatility change points inform cryptocurrency investment strategies and risk management practices? Using a continuous-time lead-lag estimator and a non-parametric volatility change point detection method, we analysed daily price data for the year 2022. Our findings reveal complex lead-lag dynamics, with Polygon unexpectedly emerging as a leading indicator despite its smaller market capitalisation. This challenges the conventional assumption that larger cryptocurrencies like Bitcoin consistently lead market movements, indicating potential inefficiencies in information transmission within the crypto market. The volatility change point analysis identifies varying frequencies of volatility shifts across the cryptocurrencies, with Polygon experiencing the most frequent changes (7) and Bitcoin the least (3). We observe both clustering of volatility change points around significant market events and variations reflecting the unique characteristics of each cryptocurrency. Our results suggest that while the cryptocurrency market shows a high degree of interconnectedness, it also exhibits nuanced dynamics that could be exploited for more effective hedging strategies and improved risk assessment. The study highlights the rapid evolution of the cryptocurrency ecosystem, where technological factors and market-specific events can significantly influence price dynamics and volatility patterns.
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