Multicollinearity Mitigation

Multicollinearity occurs when independent variables in a model are highly correlated, making it difficult to isolate the effect of each variable on the target. This can lead to unstable model coefficients and make the model sensitive to small changes in the data.

In the context of derivatives, where indicators like moving averages or volatility measures often move together, this is a common issue. Mitigation strategies include removing highly correlated features, using regularization techniques like Ridge, or employing dimensionality reduction methods like PCA.

By reducing multicollinearity, the model becomes more interpretable and robust. It ensures that the model's predictions are based on reliable and independent sources of information.

This is essential for building stable and trustworthy quantitative strategies.