一种自组织混合模型在汇率波动性预测中的应用

Exchange rate volatility prediction by an extended self-organizing mixture model

中文关键词  自组织神经网络  波动性  汇率  局部建模

英文关键词  self-organizing neural network  volatility  exchange rate  local model

中文摘要

      汇率波动性的预测一直以来是研究金融市场者关注的焦点之一, 本文拓展了一种基于自组织神经网络技术的, 用于预测非平稳汇率波动性的自组织混合模型(SOMAR). SOMAR突破了传统模型对平稳性的假设, 变全局建模为局部建模, 使得全局非平稳数据变成局部平稳数据. 同时, 它也是一种基于神经元网络技术的非参数回归模型, 结合传统回归模型的简易性和神经元网络算法的灵活性, 拓展模型(ESOMAR)提高了对数据异构的适应性. 在对汇率波动性的预测实验中, ESOMAR体现出优于传统回归模型和一些基于其它神经元网络模型的效果, 并证明了它在预测金融数据方面所具有的价值.

英文摘要

      Exchange rate volatility prediction has long been the issue that most financial market researchers are concerned with. This article applies a self-organizing-map-based method to the self-organizing-mixture model(SOMAR) for predicting the non-stationary volatility of daily exchange rate. This extended SOMAR(ESOMAR) model is free from the constraint of stationarity which is required by most of the traditional regressive models; it also replaces the global modeling by the local modeling by splitting a non-stationary time series into piece-wise stationary time series episodes. Meanwhile, ESOMAR is a non-parametric neural network regressive model; it combines the simplicity of the traditional regressive model and the flexibility of neural networks, making it adaptive to the heterogeneous data. The prediction results of exchange rate volatility show that the ESOMAR outperforms many traditional regressive models as well as other neural-network-based approaches.