Huber loss for contaminated observations

Why might Huber loss help when a return dataset contains a few extreme residuals? What must still be validated?

Huber loss is quadratic near zero and linear outside a threshold, limiting the influence of very large residuals relative to squared loss. It can improve robustness to contamination, but genuine tail events may be economically important. Tune the threshold inside training validation, investigate data errors separately, and assess tail-period performance. Robust fitting does not eliminate the need for a defensible target and execution objective.