Larisa Yarovaya

Empirical cryptocurrency researchers frequently use concepts of mean reversion, trend and cointegration to characterise price dynamics and tests of market inefficiency. This paper introduces a broader memory‐driven framework to examine how the concept of market efficiency has evolved over time, especially by characterising varied mean‐reversion strategies with slow‐paced error corrections to identify a semi‐strong market efficiency in crypto markets. We find strong evidence that market efficiency in Bitcoin (BTC) and Ethereum (ETH) is heterogeneous and time‐varying across all frequencies. Event shocks and structural breaks exert substantial influence on abrupt changes in efficiency, while results at the 240‐min sampling interval show robustness. The findings suggest that policymakers should time interventions to mitigate lag effects, release policy during low‐leverage periods and closely monitor the two distinct waves of efficiency breaks as well as cross‐frequency risk exposure. For practitioners, linking efficiency dynamics to trading strategies can enhance risk management and statistical arbitrage opportunities, while scholars can build advanced regime‐switching models informed by these empirical patterns. This paper provides new insight into price patterns and market efficiency dynamics, contributing to the understanding of the complex interplay between event shocks, structural breaks and regime switching of market efficiency in cryptocurrency markets.

As the crypto-asset ecosystem matures, the use of high-frequency data has become increasingly common in decentralized finance literature. Using bibliometric analysis, we characterize the existing cryptocurrency literature that employs high-frequency data. We highlighted the most influential authors, articles, and journals based on 189 articles from the Scopus database from 2015 to 2022. This approach enables us to identify emerging trends and research hotspots with the aid of co-citation and cartographic analyses. It shows knowledge expansion through authors’ collaboration in cryptocurrency research with co-authorship analysis. We identify four major streams of research: (i) return prediction and measurement of cryptocurrency volatility, (ii) (in)efficiency of cryptocurrencies, (iii) price dynamics and bubbles in cryptocurrencies, and (iv) the diversification, safe haven, and hedging properties of Bitcoin. We conclude that highly traded cryptocurrencies’ investment features and economic outcomes are analyzed predominantly on a tick-by-tick basis. This study also provides recommendations for future studies.