Paper Title:
Modeling and Prediction of the CNY Exchange Rates Using RBF Neural Networks versus GARCH Models
  Abstract

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 GBP-CNY 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.

  Info
Periodical
Edited by
Yuanzhi Wang
Pages
375-382
DOI
10.4028/www.scientific.net/AMM.39.375
Citation
Z. C. Liu, X. Y. Liu, Z. R. Zheng, "Modeling and Prediction of the CNY Exchange Rates Using RBF Neural Networks versus GARCH Models", Applied Mechanics and Materials, Vol. 39, pp. 375-382, 2011
Online since
November 2010
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