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Some Statistical Models vs. Models Based on SC Applied to Daily Exchange Rates

机译：一些统计模型与基于SC的模型应用于日常汇率

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We developed forecasting models based on the advanced statistical (stochastic) and soft computing (computational intelligence) techniques for predicting high frequency data sets. Firstly, we used the standard statistical tools such as the autocorrelation/partial autocorrelation function, clustering, etc. to identify the chunks of information that are deemed essential for knowledge representation. Afterwards, (1) we proposed statistical methods to identify the relationship between the information granules; (2) based on the platform of granular or soft computing (G or SC) we developed and formal expressed the underlying mechanisms (models) that generate the observed data and, in turn, to forecast future values of the investigated process in managerial decision-making. The proposed intelligent approach is applied to the time series of USD/EUR exchange rates. We found that it is possible to achieve significant risk reduction in managerial decision-making by applying intelligent forecasting models based on the latest information technologies. We show that statistical GARCH-class models can identify the presence of the leverage effect and to react to the good and bad news. In a comparative study is shown, that both presented modeling approaches are able to model and predict high frequency data with reasonable accuracy, but the neural network approach is more effective and accurate.
机译：我们开发了基于先进的统计（随机）和软计算（计算智能）技术的预测模型，用于预测高频数据集。首先，我们使用了诸如自相关/部分自相关函数，聚类等的标准统计工具来识别被视为知识表示必不可少的信息的块。之后，（1）我们提出了统计方法，以确定信息颗粒之间的关系; （2）基于粒度或软计算的平台（G或SC），我们开发并正式表达了产生所观察到的数据的潜在机制（模型），并反过来，反过来，预测管理决策中调查过程的未来价值 - 制作。建议的智能方法适用于美元/欧元汇率的时间序列。我们发现，通过基于最新信息技术应用智能预测模型，可以实现管理决策的重大风险。我们展示统计加基级模型可以识别杠杆效应的存在，并对好的和坏消息作出反应。在显示比较研究中，呈现的两种建模方法都能够以合理的准确度模拟和预测高频数据，但是神经网络方法更有效和准确。

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