Modeling and Prediction of the CNY Exchange Rates Using RBF Neural Networks versus GARCH Models

机译：利用RBF神经网络与GARCH模型建模与预测CNY汇率

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The CNY exchange rates can be viewed as financial time series which are characterized by high uncertainty, nonlinearity and time-varying behavior. Predictions for CNY exchange rates of GBPCNY and USD-CNY were carried out respectively by means of RBF neural network forecasters and GARCH models. GARCH is a mechanism that includes past variances in the explanation of future variances and a time-series technique that we use to model the serial dependence of volatility. The detailed design of architectures of RBF neural network models, transfer functions of the hidden layer nodes, input vectors and output vectors were made with many tests. While experimental results show that the performance of RBF neural networks for forecasting spot CNY exchange rates is better than that of GARCH, both of them are acceptable and effective especially in short term predictions.

机译：CNY汇率可以被视为金融时间系列，其特征在于具有高不确定性，非线性和时变行为的特征。通过RBF神经网络预测员和GARCH模型，分别进行了GBPCNY和USD-CNY的人民币汇率的预测。 GARCH是一种机制，包括过去的差异在未来的差异和时间序列技术中，我们用于模拟波动率的串行依赖性。 RBF神经网络模型的架构的详细设计，隐藏层节点的传递函数，输入向量和输出向量进行了许多测试。虽然实验结果表明，用于预测现场CNY汇率的RBF神经网络的性能优于GARCH的性能，但它们都是可接受的，特别是在短期预测中有效。

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《International Symposium on Quantum, Nano and Micro Technologies》|2011年||共8页
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Zhaocheng LIU; Xiyu LIU; Ziran ZHENG;
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中图分类 TB3-53;
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CNY exchange rate; RBF neural network; Financial time series; Forecaster; GARCH model; Heteroscedasticity;

机译：CNY汇率;RBF神经网络;金融时间系列;预测;GARCH模型;异源性;

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Modeling and Prediction of the CNY Exchange Rates Using RBF Neural Networks versus GARCH Models

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