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

Jong-Min Kim, Chanho Cho and Chulhee Jun

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.

MoreLess

Year of publication:	2022
Authors:	Kim, Jong-Min ; Cho, Chan Ho ; Jun, Chulhee
Published in:	Journal of risk and financial management : JRFM. - Basel : MDPI, ISSN 1911-8074, ZDB-ID 2739117-6. - Vol. 15.2022, 2, Art.-No. 74, p. 1-10
Subject:	cryptocurrencies \| Bitcoin \| error correction model \| Granger causality \| Virtuelle Währung \| Virtual currency \| Kointegration \| Cointegration \| Kausalanalyse \| Causality analysis \| Prognoseverfahren \| Forecasting model \| Theorie \| Theory

freely available

Full text |

More access options

www.mdpi.com

doi.org

Check Google Scholar

In libraries world-wide (WorldCat)

In German libraries (KVK)

subito order

I need help

Type of publication:	Article
Type of publication (narrower categories):	Aufsatz in Zeitschrift ; Article in journal
Language:	English
Other identifiers:	10.3390/jrfm15020074 [DOI] hdl:10419/258797 [Handle]
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10012821349