This paper proposes an innovative semiparametric nonlinear fuzzy-EGARCH-ANN model to solve the problem of accurate modeling for forecasting stock market volatility. This model has been developed by a combination of the FIS, ANN, and EGARCH models. Because the proposed model is highly nonlinear and gradient-based parameter estimation methods might not give global optimal parameters for highly nonlinear models, the study has decided to use evolutionary algorithms instead. In particular, a differential evolution (DE) algorithm is suggested to solve the parameter estimation problem of the proposed model. After this, the semiparametric nonlinear fuzzy-EGARCH-ANN model has been developed mathematically from the three models mentioned before, and the study has simulated data by it. After the simulation, parameter estimation of the proposed model using a differential evolution algorithm on the simulated data is done. Finally, it is seen that the proposed model is good in capturing the volatility clustering and leverage effects of highly nonlinear and complicated financial time series data that were overlooked by the EGARCH model.

中文翻译：

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利用人工神经网络和模糊推理系统改进EGARCH模型的预测

本文提出了一种创新的半参数非线性模糊-EGARCH-ANN模型解决了预测股市波动的准确建模问题。该模型是通过FIS，ANN和EGARCH模型的组合开发的。由于所提出的模型是高度非线性的，并且基于梯度的参数估计方法可能无法为高度非线性模型提供全局最优参数，因此该研究决定改为使用进化算法。特别地，提出了一种差分进化算法（DE）来解决所提出模型的参数估计问题。此后，从前面提到的三个模型中数学地建立了半参数非线性模糊-EGARCH-ANN模型，并以此为基础进行了仿真研究。在仿真之后，使用差分进化算法对仿真数据进行所提出模型的参数估计。最后，