Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/286589 
Year of Publication: 
2024
Series/Report no.: 
IHS Working Paper No. 52
Publisher: 
Institut für Höhere Studien - Institute for Advanced Studies (IHS), Vienna
Abstract: 
We examine two major topics in the field of cryptocurrencies. On the one hand, we investigate possible long-run equilibrium relationships among ten major cryptocurrencies by applying two different cointegration tests. This analysis aims at constructing cointegrated portfolios that enable statistical arbitrage. Moreover, we find evidence for a connection between market volatility and the spread used for trading. The results of the trading strategies suggest that cointegrated portfolios based on the Johansen procedure generate the highest abnormal log-returns, both in-sample and out-of-sample. Five out of six trading strategies generate a positive overall profit and outperform a passive investment approach out-of-sample. The second part of the econometric analysis explores Granger causality between volatility and the spread. For this analysis, we implement two types of forecasting models for Bitcoin volatility: the GARCH (generalized autoregressive conditional heteroskedasticity) family using daily price data and the HAR (Heterogeneous AutoRegressive) model family based on 5-min high-frequency data. In both categories, we also consider potential jumps in the price series, as we found that price jumps play an important role in Bitcoin volatility forecasts. The findings indicate that the realized GARCH model is the only GARCH model that can compete against the HAR-RV (Heterogeneous Autoregressive Realized Volatility) model in out-of-sample forecasting.
Subjects: 
cryptocurrencies
bitcoin volatility
realized variance
jump variation
cointegrated portfolios
statistical arbitrage
JEL: 
C22
C52
C53
Creative Commons License: 
cc-by Logo
Document Type: 
Working Paper

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