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Exchange Rate Forecasting Using Entropy Optimized Multivariate Wavelet Denoising Model

机译：熵优化的多元小波降噪模型预测汇率

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摘要

Exchange rate is one of the key variables in the international economics and international trade. Its movement constitutes one of the most important dynamic systems, characterized by nonlinear behaviors. It becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulation and global integration worldwide. Facing the increasingly diversified and more integrated market environment, the forecasting model in the exchange markets needs to address the individual and interdependent heterogeneity. In this paper, we propose the heterogeneous market hypothesis- (HMH-) based exchange rate modeling methodology to model the micromarket structure. Then we further propose the entropy optimized wavelet-based forecasting algorithm under the proposed methodology to forecast the exchange rate movement. The multivariate wavelet denoising algorithm is used to separate and extract the underlying data components with distinct features, which are modeled with multivariate time series models of different specifications and parameters. The maximum entropy is introduced to select the best basis and model parameters to construct the most effective forecasting algorithm. Empirical studies in both Chinese and European markets have been conducted to confirm the significant performance improvement when the proposed model is tested against the benchmark models.
机译：汇率是国际经济学和国际贸易中的关键变量之一。它的运动构成了最重要的动力学系统之一，以非线性行为为特征。随着全球范围内放松管制和全球一体化的程度越来越高，它变得越来越不稳定，并且对日益多样化的影响因素更加敏感。面对日益多样化和一体化的市场环境，交易所市场的预测模型需要解决个体和相互依存的异质性。在本文中，我们提出了基于异质市场假设（HMH-）的汇率建模方法来对微观市场结构进行建模。然后，在所提出的方法论基础上，进一步提出了基于熵优化的基于小波的预测算法来预测汇率变动。多元小波去噪算法用于分离和提取具有不同特征的基础数据分量，这些特征使用具有不同规格和参数的多元时间序列模型进行建模。引入最大熵来选择最佳基础和模型参数，以构建最有效的预测算法。已经在中国和欧洲市场进行了实证研究，以确认将建议的模型与基准模型进行测试时性能的显着提高。

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  • 来源
    《Mathematical Problems in Engineering》 |2014年第8期|389598.1-389598.9|共9页
  • 作者

    Kaijian He; Lijun Wang; Yingchao Zou; Kin Keung Lai;

  • 作者单位

    School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China,Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Kowloon, Hong Kong;

    School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China;

    School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China;

    Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Kowloon, Hong Kong,School of Economics and Management, North China Electric Power University, Beijing 102206, China;

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