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首页> 外文会议>International Scientific Conference on Business, Economics, and Management >Application of Neural Network Models in Modelling Economic Time Series with Non-constant Volatility
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Application of Neural Network Models in Modelling Economic Time Series with Non-constant Volatility

机译：神经网络模型在模拟经济时序序列与非恒定波动性的应用

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In this paper, we investigate the volatility dynamics of EUR/GBP currency using statistical as well as the neural network approach which is an alternative way for time series modelling and forecasting in economics. The goal of this paper is to provide an alternative and reasonable way in modelling dynamic economic time series. We suggest an alternative approach for forecasting time series with non-constant volatility-we suggest and implement several neural network prediction models; we also use a large number of statistical models as well as different optimization techniques for artificial neural network. After discussing the basics of statistical volatility modelling and the basis of artificial neural networks we perform the experiment on real financial data. We quantify several ARCH and GARCH models; we also implement various RBF neural network prediction models. The comparative analysis of out-of-sample forecasts evaluated using MSE evaluation measures is performed. Finally, we state that suggested neural network models performed almost as good as the standard statistical models and are therefore reasonable and acceptable in economic modelling.
机译：在本文中，我们使用统计以及神经网络方法调查EUR / GBP货币的波动性动态，这是时间序列建模和经济学预测的替代方法。本文的目标是提供一种替代和合理的方式，可以在模拟动态经济时序序列。我们建议采用非恒定波动率预测时间序列的替代方法 - 我们建议并实施了几种神经网络预测模型;我们还使用大量的统计模型以及用于人工神经网络的不同优化技术。在讨论统计波动建模的基础之后和人工神经网络的基础之后，我们对实际财务数据进行实验。我们量化了几种拱形和加奇模型;我们还实现了各种RBF神经网络预测模型。进行使用MSE评估措施评估的样本预测的比较分析。最后，我们说明的建议的神经网络模型几乎与标准统计模型一样好，因此在经济建模中具有合理和可接受的。

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