Shrinkage Methods

Shrinkage methods are statistical techniques that reduce the variance of model estimates by shrinking coefficients toward a central value. These methods are widely used in quantitative finance to improve the performance of predictive models, especially when the number of predictors is large relative to the number of observations.

By reducing the magnitude of the coefficients, these methods prevent the model from over-relying on any single noisy input. Common examples include Lasso and Ridge regression, each offering different ways to handle coefficient shrinkage.

These techniques are essential for creating models that perform consistently across different market regimes. They are the primary defense against the dangers of overfitting in complex financial datasets.