Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2021 019

Understanding jumps in high frequency digital asset markets

Danial Saef
Odett Nagy
Sergej Sizov
Wolfgang Karl Härdle

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 technology.

jumps, market microstructure noise, high frequency data, cryptocurrencies, CRIX,
option pricing

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