Combining deep learning with econometric models: volatility forecasting using the KAN-GARCH-MIDAS framework
Year: 2025
Type: article
Abstract: 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.
Source: Journal of Applied Economics
Authors: Ting Liu, Weichong Choo, Han Xinping, Le Li
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,533
OpenAlex ID: W4414094244