A New Forecasting Model for USD/CNY Exchange Rate

Cai, Zucong; Chen, Linna; Fang, Ying

doi:10.1515/1558-3708.1878

Cited by 12 publications

(1 citation statement)

References 36 publications

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“…GBP/CNY and USD/CNY exchange rate predictions were carried out by Liu et al (2011) using predictions by RBF neural networks and GARCH models. CNY rates can be considered as a financial TS (time series) characterized by a high non-linearity and a change of behavior over time (Cai et al 2012). CNY has grown from a trading currency to an investment currency and currently has the potential to be a worldwide reserve currency.…”

Section: Literature Reviewmentioning

confidence: 99%

Use of Neural Networks to Accommodate Seasonal Fluctuations When Equalizing Time Series for the CZK/RMB Exchange Rate

Rowland

Lăzăroiu

Podhorská

2020

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The global nature of the Czech economy means that quantitative knowledge of the influence of the exchange rate provides useful information for all participants in the international economy. Systematic and academic research show that the issue of estimating the Czech crown/Chinese yuan exchange rate, with consideration for seasonal fluctuations, has yet to be dealt with in detail. The aim of this contribution is to present a methodology based on neural networks that takes into consideration seasonal fluctuations when equalizing time series by using the Czech crown and Chinese yuan as examples. The analysis was conducted using daily information on the Czech crown/Chinese yuan exchange rate over a period of more than nine years. This is the equivalent of 3303 data inputs. Statistica software, version 12 by Dell Inc. was used to process the input data and, subsequently, to generate multi-layer perceptron networks and radial basis function neural networks. Two versions of neural structures were produced for regression purposes, the second of which used seasonal fluctuations as a categorical variable–year, month, day of the month and week—when the value was measured. All the generated and retained networks had the ability to equalize the analyzed time series, although the second variant demonstrated higher efficiency. The results indicate that additional variables help the equalized time series to retain order and precision. Of further interest is the finding that multi-layer perceptron networks are more efficient than radial basis function neural networks.

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Section: Literature Reviewmentioning

confidence: 99%