Understanding jumps in high frequency digital asset markets

TL;DR

This paper investigates high frequency jumps in digital asset markets, revealing their clustering around black swan events and their impact on end-of-day returns, with implications for crypto option pricing.

Contribution

It provides empirical evidence of jump clustering and their effects on returns, highlighting the need for improved econometric methods for crypto markets.

Findings

Cross market digital asset returns are driven by high frequency jumps.

Jumps are clustered around black swan events, similar to volatility patterns.

Intra-day jumps significantly influence end-of-day returns.

Abstract

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.