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Open AccessArticle

Hybrid Forecasting Models Based on the Neural Networks for the Volatility of Bitcoin

by Monghwan Seo 1 and Geonwoo Kim 2,*
1
Department of Mathematics, Yonsei University, 50 Yonsei-ro Seodaemun-gu, Seoul 03722, Korea
2
School of Liberal Arts, Seoul National University of Science and Technology, Seoul 01811, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(14), 4768; https://doi.org/10.3390/app10144768
Received: 10 June 2020 / Revised: 7 July 2020 / Accepted: 9 July 2020 / Published: 10 July 2020
(This article belongs to the Special Issue Applied Machine Learning)
In this paper, we study the volatility forecasts in the Bitcoin market, which has become popular in the global market in recent years. Since the volatility forecasts help trading decisions of traders who want a profit, the volatility forecasting is an important task in the market. For the improvement of the forecasting accuracy of Bitcoin’s volatility, we develop the hybrid forecasting models combining the GARCH family models with the machine learning (ML) approach. Specifically, we adopt Artificial Neural Network (ANN) and Higher Order Neural Network (HONN) for the ML approach and construct the hybrid models using the outputs of the GARCH models and several relevant variables as input variables. We carry out many experiments based on the proposed models and compare the forecasting accuracy of the models. In addition, we provide the Model Confidence Set (MCS) test to find statistically the best model. The results show that the hybrid models based on HONN provide more accurate forecasts than the other models. View Full-Text
Keywords: Bitcoin; artificial neural network; higher order neural network; volatility forecasting; hybrid models Bitcoin; artificial neural network; higher order neural network; volatility forecasting; hybrid models
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Seo, M.; Kim, G. Hybrid Forecasting Models Based on the Neural Networks for the Volatility of Bitcoin. Appl. Sci. 2020, 10, 4768.

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