Cointegrated portfolios and volatility modeling in the cryptocurrency market

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.