Forecasting the price of the cryptocurrency using linear and nonlinear error correction model

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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Year of publication:	2022
Authors:	Kim, Jong-Min ; Cho, Chan Ho ; Jun, Chulhee
Published in:	Journal of Risk and Financial Management. - Basel : MDPI, ISSN 1911-8074. - Vol. 15.2022, 2, p. 1-10
Publisher:	Basel : MDPI
Subject:	cryptocurrencies \| Bitcoin \| error correction model \| Granger causality

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Type of publication:	Article
Type of publication (narrower categories):	Article
Language:	English
Other identifiers:	10.3390/jrfm15020074 [DOI] 1793813957 [GVK] hdl:10419/258797 [Handle]
Source:	EconStor

Persistent link: https://ebvufind01.dmz1.zbw.eu/10013201377