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

Jong Min Kim, Chanho Cho, Chulhee Jun

Research output: Contribution to journalArticlepeer-review

8 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 languageEnglish (US)
Article number74
JournalJournal of Risk and Financial Management
Volume15
Issue number2
DOIs
StatePublished - Feb 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Bitcoin
  • Granger causality
  • cryptocurrencies
  • error correction model

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