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

Jong Min Kim; Chanho Cho; Chulhee Jun

doi:10.3390/jrfm15020074

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

Jong Min Kim, Chanho Cho, Chulhee Jun

Statistics (Morris)

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

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.

Original language	English (US)
Article number	74
Journal	Journal of Risk and Financial Management
Volume	15
Issue number	2
DOIs	https://doi.org/10.3390/jrfm15020074
State	Published - Feb 2022

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© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

Bitcoin
Granger causality
cryptocurrencies
error correction model

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10.3390/jrfm15020074

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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.",

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

Abstract

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