High-Frequency Crypto Jumps and Their Effect on Daily Returns

Summary

This study examines whether jumps in high-frequency digital-asset prices help explain returns across crypto markets. It focuses on jumps that cluster around exceptional market events and considers how their timing relates to recurring patterns in volatility and trading volume. The analysis is motivated by prior evidence on Bitcoin and attention, while extending the question to other digital assets.

Regression results indicate that intraday jumps significantly affect end-of-day returns, both in magnitude and direction. The findings may inform research on crypto options pricing, where jump behavior can matter for modeling price changes. The document also cautions that crypto markets have distinctive microstructure, so econometric methods developed for other markets may not capture their behavior well. It provides no detailed sample dates, asset list, model specifications, or estimates, which limits assessment of how broadly the results apply.

Key ideas

• High-frequency crypto returns may contain jumps clustered around exceptional events.
• Intraday jumps are reported to influence daily returns in both size and direction.
• The study relates jump behavior to recurring volatility and trading-volume patterns.
• Crypto-specific market microstructure may require specialized econometric methods.
• The results offer a potential input to crypto options pricing research.

Tags

# Understanding jumps in high frequency digital asset markets


# Understanding jumps in high frequency digital asset markets









While attention is a predictor for digital asset prices, and jumps in Bitcoin prices are well-known, we know little about its alternatives. Studying high frequency crypto data gives us the unique possibility to confirm that cross market digital asset returns are driven by high frequency jumps clustered around black swan events, resembling volatility and trading volume seasonalities. Regressions show that intra-day jumps significantly influence end of day returns in size and direction. This provides fundamental research for crypto option pricing models. However, we need better econometric methods for capturing the specific market microstructure of cryptos. All calculations are reproducible via the quantlet.com technology.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.