Bitcoin wild moves: Evidence from order flow toxicity and price jumps

Abstract

This study investigates the dynamic relationship between order flow toxicity, measured by the volume-synchronized probability of informed trading (VPIN), and price jumps in the Bitcoin market using high-frequency data and vector autoregressive model (VAR) modelling. By integrating behavioral finance theory to market microstructure framework, we explore how informed trading activity influences jumps in price, and how traders respond to such volatility. Our findings reveal that VPIN significantly predicts future price jumps, with positive serial correlation observed in both VPIN and jump size, suggesting persistent asymmetric information and momentum effects. On the contrary, price jumps occasionally affect VPIN. This study also identifies time-zone and day-of-the-week effects in VPIN, highlighting the role of global trading patterns. The results are robust among the choices of jump tests including Jiang and Oomen (2008) test which is empirically robust against market microstructure noise. These results contribute to a deeper understanding of intraday volatility in cryptocurrency markets and offer practical implications for risk management, trading strategy design, and regulatory oversight.