Financial Risk Manager Part 1

Explanation

When multicollinearity is present in a model, the coefficients of the variables tend to be jointly statistically significant. This is often evidenced by the F-statistic of the regression. The F-statistic is a measure of how well the regression model fits the data. A high F-statistic indicates that the model explains a large amount of the variation in the dependent variable. When multicollinearity is present, the variables are highly correlated, which means they collectively contribute to explaining the dependent variable, even though individual coefficients may be imprecise or unstable.

Analysis of Other Options:

• Option A: While multicollinearity does pose technical problems, this statement is too broad and doesn't capture the specific characteristic mentioned in the correct answer.
• Option C: This is incorrect because when VIF is high (typically VIF > 10), it indicates severe multicollinearity and the variable should be considered for exclusion, not inclusion.
• Option D: This is incorrect because multicollinearity actually leads to imprecise coefficient estimates and inflated standard errors, making the model less reliable.

The key insight is that multicollinearity affects individual coefficient estimates but often preserves the joint significance of the correlated variables in explaining the dependent variable.