Model Regularization

Model regularization is a set of techniques used to prevent overfitting by penalizing overly complex models during the training process. In quantitative finance, this involves adding a penalty term to the loss function that discourages the model from assigning high weights to unimportant or noisy variables.

By constraining the complexity of the model, regularization forces it to focus on the most robust and persistent features of the market data. This is essential for derivatives pricing, where models must remain stable even when market inputs are volatile or sparse.

Common methods include Lasso and Ridge regression, which shrink coefficients to reduce the impact of less predictive variables. Without regularization, a model might perfectly track past price action but fail to generalize to future volatility, leading to catastrophic risk management failures.