What does it imply when variance inflation factors (VIF) are significantly high?

When variance inflation factors (VIF) are significantly high, it indicates that the predictors in a regression model may be linearly correlated with each other. VIF quantifies how much the variance of a regression coefficient is increased due to multicollinearity, which occurs when two or more predictors are highly correlated. High VIF values suggest that a predictor's variance is inflated because of near-linear relationships with other predictors, potentially leading to unreliable coefficient estimates.

Therefore, a high VIF signals the presence of multicollinearity, which may complicate the interpretation of individual predictors’ effects on the response variable. The situation can require corrective action, such as removing or combining correlated predictors, to improve the model's reliability and interpretability. This understanding is critical in risk modeling as it affects predictions, interpretations, and decision-making processes based on the model.