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All Journal Jambura Journal of Mathematics Journal of Data Insights
Tintrim Dwi Ary Widhianingsih
Institut Teknologi Sepuluh Nopember
Computer Science & IT Mathematics

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A Hybrid LSTM with GARCH-MIDAS-X for Modelling IDX Composite Volatility: Model LSTM dengan GARCH-MIDAS-X untuk Pemodelan Volatilitas Komposit IDX Silviya Indriyani; Irhamah; Tintrim Dwi Ary Widhianingsih
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1180

Abstract

Stock market volatility forecasting plays a crucial role in supporting investment decision-making and risk management under uncertain market conditions. This study proposes a hybrid LSTM with GARCH-to modelling IDX Composite volatility. The GARCH-MIDAS-X model is first employed to decompose stock return volatility into short-run and long-run components while incorporating multiple low-frequency exogenous variables, including market news sentiment, crude oil prices, and exchange rates. The residual generated by the GARCH-MIDAS-X model is subsequently used as input for the LSTM network to capture complex nonlinear patterns and temporal dependencies that may not be fully explained by the econometric model. Model performance is evaluated through both in-sample and out-of-sample forecasting using several accuracy measures, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The empirical results indicate that the hybrid model produces forecasting performance comparable to that of the GARCH-MIDAS-X model, with only marginal differences in prediction accuracy. These findings suggest that the GARCH-MIDAS-X model is capable of capturing most of the relevant volatility dynamics, while the addition of the LSTM component provides limited incremental forecasting benefits for the observed period. Therefore, the hybrid approach may serve as an alternative forecasting framework, although its superiority over the standalone econometric model is not evident in this study.
Geographically and Temporally Weighted Log-Logistic 3-Parameter Regression Model for Poverty Severity Index : A Case Study on East Java Province Nur Huda; Purhadi Purhadi; Tintrim Dwi Ary Widhianingsih
Jambura Journal of Mathematics Vol 8, No 2: August 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v8i2.38021

Abstract

This study proposes the Geographically and Temporally Weighted Log Logistic 3 Parameter Regression (GTWLL3R) model as a novel extension of LL3R that simultaneously captures spatial and temporal heterogeneity in poverty severity index. Using the poverty severity index of East Java Province for 2022–2024, local parameters were estimated through an fixed Gaussian kernel weighting matrix based on spatial and temporal distances, with optimization using the Newton–Raphson algorithm. Model performance was evaluated using the corrected Akaike Information Criterion (AICc). The results show that GTWLL3R outperformed the LL3R and GWLL3R models, achieving the lowest AICc value of 18.311, which indicates substantially better model fit and stronger explanatory capability. The estimated coefficients vary across districts/cities and time periods, revealing different patterns of predictor effects on poverty severity index. Based on significant predictor variables, the districts/cities were classified into three clusters. These findings demonstrate that integrating LL3R into the GTWLL3R framework provides a more flexible and accurate approach for analyzing spatiotemporal poverty dynamics and offers stronger evidence for targeted poverty alleviation policies.
Co-Authors Irhamah Nur Huda Purhadi Purhadi Silviya Indriyani