Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258797 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 15 [Issue:] 2 [Article No.:] 74 [Publisher:] MDPI [Place:] Basel [Year:] 2022 [Pages:] 1-10
Publisher: 
MDPI, Basel
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.
Subjects: 
cryptocurrencies
Bitcoin
error correction model
Granger causality
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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