Abstract
We employed linear and nonlinear error correction models (ECMs) to predict the log returns of Bitcoin (BTC). The linear ECM is the best model for predicting BTC compared to the neural network and autoregressive models in terms of RMSE, MAE, and MAPE. Using a linear ECM, we are able to understand how BTC is affected by other coins. In addition, we performed Granger-causality tests on fourteen cryptocurrencies.
Highlights
The Coronavirus Disease 2019 (COVID-19) pandemic made the investment environment more uncertain
Various error correction models have been applied to the cryptocurrency market
Olmo (2020) proposed an empirical model for analyzing the dynamics of Bitcoin prices by considering a VEC model over two overlapping periods: 2010–2017 and 2010–2019. These findings provided empirical evidence on the presence of a correction in the price of Bitcoin during the period 2018–2019 uncorrelated to market fundamentals
Summary
The Coronavirus Disease 2019 (COVID-19) pandemic made the investment environment more uncertain. Haffar and Fur (2021) analyzed the impact of shocks in the financial markets of emerging and developed countries on the price of Bitcoin using a structural vector error correction model. Keilbar and Zhang (2021) analyzed the role of cointegration relationships within a large system of cryptocurrencies using a vector error correction model (VECM) framework. Olmo (2020) proposed an empirical model for analyzing the dynamics of Bitcoin prices by considering a VEC model over two overlapping periods: 2010–2017 and 2010–2019 These findings provided empirical evidence on the presence of a correction in the price of Bitcoin during the period 2018–2019 uncorrelated to market fundamentals. Performed a cointegration analysis and used a VEC model to demonstrate that there is a relationship between price of Bitcoin and some variables, including stock price index, the price of oil, and the daily trading volume of Bitcoin. The illustrated comparison study for the proposed methods is performed in terms of the measures of errors is in Section 4, with the conclusion presented Section 5
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