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Predictive Expectile Regression with SCAD for High-Dimensional Financial Big Data: Evidence from the S&P 500 | ||
| AL-Qadisiyah Journal For Administrative and Economic sciences | ||
| Volume 28, Issue 2, July 2026, Pages 567-581 PDF (3.11 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.33916/qjae.2026.02567580 | ||
| Authors | ||
| Saif H. Raheem; Maryam Hussien Kuman | ||
| University of Al-Qadisiyah | ||
| Abstract | ||
| This study proposes a Predictive Expectile Regression model with SCAD penalty to improve forecasting accuracy and variable selection in high-dimensional financial data. By integrating the SCAD regularization into expectile regression, the model effectively captures tail risk and manages multicollinearity in non-Gaussian environments. Simulation experiments under normal, skewed, and heavy-tailed errors show that the proposed ER–SCAD consistently achieves the lowest MSE and MAED, confirming its robustness and sparsity compared with ER, ER–LASSO, and ER–EN. Application to S&P 500 daily data (2015–2024) further demonstrates superior predictive performance and numerical stability. The findings indicate that combining expectile regression with SCAD penalty provides an efficient and interpretable framework for high-dimensional financial prediction and risk assessment. | ||
| Keywords | ||
| Expectile Regression; SCAD Penalty; High-Dimensional Data; Financial Forecasting; S&P 500 | ||
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