Abstract
This research analyses high-frequency data of the cryptocurrency market in regards to intraday trading patterns related to algorithmic trading and its impact on the European cryptocurrency market. 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 with respect to temporal aspects. 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 quantitative view on approaching the predictability of economic value in this new digital market.
Highlights
To understand the dynamics of this new high-frequency market, it is mandatory to investigate the statistical properties of various high-frequency variables, for example trading volume or volatility to find respective answers to questions like option pricing and forecasting
Preliminary research to visualize the cryptocurrency market was done by Trimborn and Hardle (2018) with the CRyptocurrency IndeX, CRIX, in order to represent the performance of the cryptocurrency market with the help of the most mature and accepted cryptocurrencies, such as bitcoin (BTC), ethereum (ETH), or ripple (XRP) - see appendix section 6.1 for further used abbreviations
We have chosen this data source, as the CRIX index family covers a range of cryptocurrencies based on different liquidity rules and various model selection criteria
Summary
High-frequency trading takes advantage of the incredible rise of computing power provided by the steady development of ever more capable structures. Schnaubelt et al (2018) analyzed limit order data from cryptocurrency exchanges Besides their recovery of common qualitative facts, they find that these data exhibit many of the properties found for classic limit order exchanges, such as a symmetric average limit order book, autocorrelation of returns only at the tick level and the timing of large trades. They find, that cryptocurrency exchanges exhibit a relatively shallow limit order book with quickly rising liquidity costs for larger volumes, many small trades and an extended distribution of limit order volume far beyond the current mid price. All presented graphical and numerical examples shown are reproducible and can be found on www.quantlet.de (Borke & Hardle, 2018) and are indicated as CCID
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