Arbeitspapier

Rise of the Machines? Intraday High-Frequency Trading Patterns of Cryptocurrencies

This research analyses high-frequency data of the cryptocurrency market in regards to intraday trading patterns. We study trading quantitatives such as returns, traded volumes, volatility periodicity, and provide summary statistics of return correlations to CRIX (CRyptocurrency IndeX), as well as respective overall high-frequency based market statistics. Our results provide mandatory insight into a market, where the grand scale employment of automated trading algorithms and the extremely rapid execution of trades might seem to be a standard based on media reports. Our findings on intraday momentum of trading patterns lead to a new view on approaching the predictability of economic value in this new digital market.

Sprache
Englisch

Erschienen in
Series: IRTG 1792 Discussion Paper ; No. 2019-020

Klassifikation
Wirtschaft
Portfolio Choice; Investment Decisions
Asset Pricing; Trading Volume; Bond Interest Rates
Information and Market Efficiency; Event Studies; Insider Trading
International Financial Markets
Pension Funds; Non-bank Financial Institutions; Financial Instruments; Institutional Investors
Thema
Cryptocurrency
High-Frequency Trading
Algorithmic Trading
Liquidity
Volatility
Price Impact
CRIX

Ereignis
Geistige Schöpfung
(wer)
Petukhina, Alla A.
Reule, Raphael C. G.
Härdle, Wolfgang Karl
Ereignis
Veröffentlichung
(wer)
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
(wo)
Berlin
(wann)
2019

Handle
Letzte Aktualisierung
20.09.2024, 08:22 MESZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Petukhina, Alla A.
  • Reule, Raphael C. G.
  • Härdle, Wolfgang Karl
  • Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

Entstanden

  • 2019

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