Minou Yari; Mohammad Reza Salehi Rad; Mohammad Bahrani
Abstract
Forecasting financial market volatility has always been a major challenge in economics and financial engineering. In this study, a hybrid approach based on FIGARCH and PLM-GARCH models combined with Long Short-Term Memory (LSTM) neural networks is proposed for modeling financial time series. The analyzed ...
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Forecasting financial market volatility has always been a major challenge in economics and financial engineering. In this study, a hybrid approach based on FIGARCH and PLM-GARCH models combined with Long Short-Term Memory (LSTM) neural networks is proposed for modeling financial time series. The analyzed dataset of the Iran energy index covers October 30, 2016, to January 25, 2023 with 1396 observations. The PLM-GARCH model is capable of identifying long-term dependencies and periodic structures in the conditional variance of time series, while the LSTM network improves prediction accuracy by learning complex and nonlinear patterns. In this approach, the PLM-GARCH model is first used to estimate volatility, and then the residuals from the model are fed as inputs into the LSTM network to extract nonlinear behaviors. Experimental results showed that the combined PLM- GARCH-LSTM model (RMSE = 0.00209, MAPE ≈ 5.1%) outperforms the FIGARCH-LSTM model (RMSE = 0.00224, MAPE ≈ 5.8%) and significantly improves prediction accuracy. These find- ings suggest that combining econometric periodic methods with deep learning can be a powerful tool for forecasting financial volatility.
Mohammad Abdollahzadeh; Ataabak Baagherzadeh Hushmandi; Parisa Nabati
Abstract
In recent years, precise analysis and prediction of financial time series data have received significant attention. While advanced linear models provide suitable predictions for short and medium-term periods, market studies have indicated that stock behavior adheres to nonlinear patterns and linear models ...
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In recent years, precise analysis and prediction of financial time series data have received significant attention. While advanced linear models provide suitable predictions for short and medium-term periods, market studies have indicated that stock behavior adheres to nonlinear patterns and linear models capturing only a portion of the market's stock behavior. Nonlinear exponential autoregressive models have proven highly practical in solving financial problems. This article introduces a new nonlinear model that allocates coefficients to significant variables. To achieve this, existing exponential autoregressive models are analyzed, tests are conducted to validate data integrity and identify influential factors in data trends, and an appropriate model is determined. Subsequently, a novel coefficient allocation method for optimizing the nonlinear exponential Autoregressive model is proposed. The article then proves the ergodicity of the new model and determines its order using the Akaike Information Criterion (AIC). Model parameters are estimated using the nonlinear least squares method. To demonstrate the performance of the proposed model, numerical simulations of Kayson Corporation's stocks are analyzed using existing methods and the new approach. The numerical simulation results confirm the effectiveness and prediction accuracy of the proposed method compared to existing approaches.
