Financial Risk Manager Part 1

Explanation

In the presence of near multicollinearity, the OLS estimator maintains the following properties:

• Consistent: As the sample size increases, the estimator converges to the true parameter value
• Unbiased: The expected value of the estimator equals the true parameter value
• Not Efficient: The variance of the estimator becomes inflated, making it less precise

Key Points:

• Multicollinearity increases the standard errors of coefficient estimates
• While the estimates remain unbiased, their precision decreases significantly
• High variance inflation factor (VIF) values indicate multicollinearity issues
• The estimator still converges to the true parameters with large sample sizes

Therefore, option C correctly describes the properties of OLS estimators under near multicollinearity conditions.