Combining deep learning with econometric models: volatility forecasting using the KAN-GARCH-MIDAS framework
Machine learning and deep learning are increasingly applied in finance, yet few studies explore how they can enhance traditional econometric models. This study proposes an innovative KAN-GM model, integrating the Kolmogorov–Arnold network (KAN) with the GARCH-MIDAS model to extract nonlinear macroec...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Taylor and Francis
2025
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/123015/1/123015.pdf http://psasir.upm.edu.my/id/eprint/123015/ https://www.tandfonline.com/doi/full/10.1080/15140326.2025.2555479 |
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| Summary: | Machine learning and deep learning are increasingly applied in finance, yet few studies explore how they can enhance traditional econometric models. This study proposes an innovative KAN-GM model, integrating the Kolmogorov–Arnold network (KAN) with the GARCH-MIDAS model to extract nonlinear macroeconomic features for volatility forecasting. Empirical results show that KAN-GM outperforms traditional GARCH in MAE and MedAE, consistently ranks in the optimal model set via MCS tests, and demonstrates strong cross-market adaptability (stocks and forex). It also maintains robustness pre- and post-COVID-19. The model effectively combines deep learning and econometrics, improving financial risk prediction. |
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