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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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    1. Georg Keilbar & Yanfen Zhang, 2021. "On cointegration and cryptocurrency dynamics," Digital Finance, Springer, vol. 3(1), pages 1-23, March.
    2. Dante Miller & Jong-Min Kim, 2021. "Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies," JRFM, MDPI, vol. 14(10), pages 1-10, October.
    3. Rodrigo Hakim das Neves, 2020. "Bitcoin pricing: impact of attractiveness variables," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-18, December.
    4. Łukasz Goczek & Ivan Skliarov, 2019. "What drives the Bitcoin price? A factor augmented error correction mechanism investigation," Applied Economics, Taylor & Francis Journals, vol. 51(59), pages 6393-6410, December.
    5. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    6. Paolo Giudici & Paolo Pagnottoni, 2020. "Vector error correction models to measure connectedness of Bitcoin exchange markets," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(1), pages 95-109, January.
    7. Haffar, Adlane & Le Fur, Eric, 2021. "Structural vector error correction modelling of Bitcoin price," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 170-178.
    8. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    9. Ahmed Ibrahim & Rasha Kashef & Menglu Li & Esteban Valencia & Eric Huang, 2020. "Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables," JRFM, MDPI, vol. 13(9), pages 1-21, August.
    10. Eric Le Fur, 2021. "Structural vector error correction modelling of Bitcoin price," Post-Print hal-03687437, HAL.
    11. Beata Szetela & Grzegorz Mentel & Yuriy Bilan & Urszula Mentel, 2021. "The relationship between trend and volume on the bitcoin market," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(1), pages 25-42, March.
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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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