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Financial markets, characterized by their complex dynamics and inherent volatility, require robust predictive models. In this paper, we investigate each behavior separately, we have implemented a fuzzy system in response to the asymmetry observed in financial time series. We used a model that incorporates the GARCH model with fuzzy systems. This approach aims to overcome the limitations of traditional models by capturing the subtleties of financial time series. We explore the impact of various volatility estimators, such as Realized Variance and Realized Kernel, on the performance of the hybrid model. Parameters are optimized using a genetic algorithm to maximize prediction accuracy. Our experiments on high-frequency financial data demonstrate the robustness of the hybrid model.

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Combining ANFIS and GARCH Models for Predicting Stock Index Returns

  • 会议论文

  • 发表日期：

    2025年

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  • Youssra Bakkali,
  • Mhamed EL Merzguioui,
  • Abdelhadi Akharif

摘要

Financial markets, characterized by their complex dynamics and inherent volatility, require robust predictive models. In this paper, we investigate each behavior separately, we have implemented a fuzzy system in response to the asymmetry observed in financial time series. We used a model that incorporates the GARCH model with fuzzy systems. This approach aims to overcome the limitations of traditional models by capturing the subtleties of financial time series. We explore the impact of various volatility estimators, such as Realized Variance and Realized Kernel, on the performance of the hybrid model. Parameters are optimized using a genetic algorithm to maximize prediction accuracy. Our experiments on high-frequency financial data demonstrate the robustness of the hybrid model.