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Robust Elastic Net Regression via Density Power Divergence for High-Dimensional Financial Data | ||
| AL-Qadisiyah Journal For Administrative and Economic sciences | ||
| Volume 28, Issue 1, April 2026, Pages 17-31 PDF (2.45 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.33916/qjae.2026.011731 | ||
| Author | ||
| Ahmed majeed fakhir | ||
| University of Al-Qadisiyah | ||
| Abstract | ||
| This study develops a Robust Elastic Net regression method based on Density Power Divergence to handle outliers, heavy-tailed noise, and high dimensionality in financial data. By combining divergence-based weighting with elastic regularization, the proposed model achieves robustness, sparsity, and stability simultaneously. Simulation results show that the method performs similarly to the classical Elastic Net under clean data, while providing substantially lower mean squared error and higher resistance to contamination. An application to S&P 500 returns confirms that the proposed approach yields more stable and interpretable coefficient estimates. These findings demonstrate the effectiveness of the Robust Elastic Net for noisy and volatile financial environments. | ||
| Keywords | ||
| Robust Elastic Net . Density Power Divergence . High; Dimensional Regression . Financial Data .Outliers | ||
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