Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/246490
Authors: 
Saef, Danial
Nagy, Odett
Sizov, Sergej
Härdle, Wolfgang
Year of Publication: 
2021
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2021-019
Publisher: 
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
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.
Subjects: 
jumps
market microstructure noise
high frequency data
cryptocurrencies
CRIX
option pricing
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.