Regularization Bias

Regularization bias refers to the deliberate error introduced into a model by the regularization process to reduce variance. By constraining the size of coefficients, the model may not fit the training data perfectly, but it becomes much better at generalizing to new, unseen market conditions.

In derivatives pricing and quantitative risk modeling, this bias is a necessary trade-off to prevent the model from capturing noise as if it were a signal. It ensures that the model remains stable even when market regimes shift suddenly.

While it slightly shifts the estimates away from the true underlying parameters, it significantly lowers the risk of extreme errors. This balance is key to creating sustainable trading strategies.