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

被引：9

作者：

Kim, Jong-Min ^{[1
]}

Cho, Chanho ^{[2
]}

Jun, Chulhee ^{[3
]}

机构：

[1] Univ Minnesota Morris, Div Sci & Math, Stat Discipline, Morris, MN 56267 USA

[2] Black Hills State Univ, Sch Business & Nat Sci, Spearfish, SD 57783 USA

[3] Bloomsburg Univ Penn, Dept Finance, Bloomsburg, PA 17815 USA

来源：

JOURNAL OF RISK AND FINANCIAL MANAGEMENT | 2022年 / 15卷 / 02期

关键词：

cryptocurrencies; Bitcoin; error correction model; Granger causality;

D O I：

10.3390/jrfm15020074

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

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.

引用

页数：10

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