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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jong-Min Kim & Chanho Cho & Chulhee Jun, 2022. "Forecasting the Price of the Cryptocurrency Using Linear and Nonlinear Error Correction Model," JRFM, MDPI, vol. 15(2), pages 1-10, February.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:2:p:74-:d:746120
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    References listed on IDEAS

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    Cited by:

    1. Mustafa Özer & Serap Kamisli & Fatih Temizel & Melik Kamisli, 2022. "Are COVID-19-Related Economic Supports One of the Drivers of Surge in Bitcoin Market? Evidence from Linear and Non-Linear Causality Tests," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
    2. Zi Ye & Yinxu Wu & Hui Chen & Yi Pan & Qingshan Jiang, 2022. "A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin," Mathematics, MDPI, vol. 10(8), pages 1-21, April.

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