This study empirically investigates a central tension in quantitative finance: the divergence between theoretically optimal and robust portfolio construction under real-world estimation uncertainty. Using a dynamic, time-varying optimization framework, we compare the performance of three...

The score-driven framework relies on pre-specified scoring rules tied to assumed conditional densities, making it vulnerable to misspecification under outliers or structural breaks. We embed the flexible Barron loss within the quasi score-driven (QSD) framework, allowing the degree of robustness...

Evaluation metrics are essential in selecting the most appropriate predictive model. Conventional metrics such as R-squared, root mean squared error (RMSE), and mean absolute percentage error (MAPE) rely on arithmetic averages, making them sensitive to outliers, undefined values, and scale...