Forecasting the Price of the Cryptocurrency Using Linear and Nonlinear Error Correction Model

Jong-Min Kim, Chanho Cho and Chulhee Jun
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Jong-Min Kim: Statistics Discipline, Division of Science and Mathematics, University of Minnesota-Morris, Morris, MN 56267, USA
Chanho Cho: School of Business and Natural Science, Black Hills State University, Spearfish, SD 57783, USA
Chulhee Jun: Department of Finance, Bloomsburg University of Pennsylvania, Bloomsburg, PA 17815, USA

JRFM, 2022, vol. 15, issue 2, 1-10

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.

Keywords: cryptocurrencies; Bitcoin; error correction model; Granger causality (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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