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首页> 外文会议>Third International Conference on Neural Networks in the Capital Markets Vol.2 London, England 11-13 October 95 >Predicting Returns on Canadian Exchange Rates With rtifical Neural Networks and Egarch-M Models
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Predicting Returns on Canadian Exchange Rates With rtifical Neural Networks and Egarch-M Models

机译：利用神经网络和Egarch-M模型预测加拿大汇率的回报

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摘要

This study investigates the problem of predicting daily returns based on five Candaian exchange rates using feedforward neural networks and EGARCH models. The statistical properties of five daily exchange rate series (US dollar, German mark, French franc, Japanese yen, and British pound) are anlayzed, EGARCH-M models on the Generalized Error Distribution (GED) are fitted to the return series, and serve as comparison standard, along with random walk modes. Backpropagation networks (BPN) using lagged returns as inputs are trained and tested. Estimated volatilities from the EGARCH-M models are used also to see if aerformance is affected. The question of spillovers in interrelated markets is investigated with networks of multiple inputs and outputs. In addiiton, Elamn-tpe recurrent networks are also trained and tested. Comparison of the various methods suggests that, depsite their simplicity, neural netowrks are similar to the EGARCH-M class of nonlinear models. but superior to random walk models, in terms of in-sample fit and out-of-smaple prediction performance.
机译：这项研究调查了使用前馈神经网络和EGARCH模型基于五种坎达汇率预测每日收益的问题。对五个每日汇率系列（美元，德国马克，法国法郎，日元和英镑）的统计属性进行了分析，将基于广义误差分布（GED）的EGARCH-M模型拟合到收益系列中，并进行服务作为比较标准，以及随机游走模式。使用滞后收益作为输入的反向传播网络（BPN）受到了培训和测试。 EGARCH-M模型的估计波动率也用于查看性能是否受到影响。通过多个投入和产出的网络研究了相关市场中的溢出问题。此外，还对Elamn-tpe循环网络进行了培训和测试。各种方法的比较表明，尽管简单，但神经网络类似于EGARCH-M类非线性模型。但在样本内拟合和样本外预测性能方面优于随机游走模型。

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