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Comparing the forecasting performance of neural network and purchasing power parity: The case of Turkey

机译:比较神经网络和购买力平价的预测性能:以土耳其为例

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摘要

Investors consider foreign exchange as being among the most significant financial markets. Many discussions regarding economic development, growth strategies and stabilization policies place real exchange rate to play the most important role in the macroeconomic adjustment mechanism. This study compares a structural model and a statistical model, namely, purchasing power parity and artificial neural network models respectively, for the long term forecasting of exchange rates. Monthly data sets for the US dollar during the period of 1986-2010 and euro during the period of 1999-2010 are used. ANN has been confirmed as an effective tool in forecasting exchange rates through the evaluation of the empirical results. A possibility of extracting hidden information from the exchange rates and using this information to predict the future has been investigated by this technique. The average behavior of the above stated loss functions are estimated to form the basis for evaluating the proposed model.
机译:投资者认为外汇是最重要的金融市场之一。关于经济发展,增长战略和稳定政策的许多讨论都将实际汇率在宏观经济调整机制中发挥了最重要的作用。本研究比较了用于长期预测汇率的结构模型和统计模型,分别是购买力平价模型和人工神经网络模型。使用了1986-2010年期间美元和1999-2010年期间欧元的每月数据集。通过对经验结果的评估,人工神经网络已被确认为预测汇率的有效工具。通过这种技术已经研究了从汇率中提取隐藏信息并使用该信息预测未来的可能性。估计上述损失函数的平均行为,以形成评估所提出模型的基础。

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