Understanding jumps in high frequency digital asset markets

Danial Saef, Odett Nagy, Sergej Sizov and Wolfgang Härdle

No 2021-019, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

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.

Keywords: jumps; market microstructure noise; high frequency data; cryptocurrencies; CRIX; option pricing (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-cwa, nep-mst and nep-pay
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:irtgdp:2021019

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