Forecasting Stock Prices Volatility with Information (An ANN-GARCH Hybrid Approach)

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Abstract

This study compares the forecast performance of volatilities between three models for forecasting stock returns: GARCH, hybrid ANN-GARCH with only GARCH output as the ANN input, and a hybrid ANN-GARCH with information. Through the extensive evaluation, the research found out that the hybrid ANN-GARCH model with information outperforms the other two models in terms of forecasting accuracy and predictive power. This study is set to find out the improvement performance of the hybrid ANN-GARCH with information vis a vis the Univariate GARCH Keywords: Stock price forecasting, GARCH, Artificial Neural Network DOI: 10.7176/RJFA/14-17-04 Publication date: September 30 th 2023
基于信息的股票价格波动预测(ANN-GARCH混合方法)
本文比较了GARCH、仅以GARCH输出作为人工神经网络输入的混合ANN-GARCH和带信息的混合ANN-GARCH三种预测股票收益模型对波动率的预测效果。通过广泛的评估,研究发现带有信息的ANN-GARCH混合模型在预测精度和预测能力方面优于其他两种模型。本研究旨在找出具有信息的混合ANN-GARCH相对于单变量GARCH的改进性能。关键词:股价预测,GARCH,人工神经网络DOI: 10.7176/RJFA/14-17-04出版日期:2023年9月30日
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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