Answer: Heteroskedasticity

## Explanation **Correct Answer: A (Heteroskedasticity)** Heteroskedasticity refers to the situation where the variance of the error terms in a regression model is not constant across all observations. This violates one of the key assumptions of ordinary least squares (OLS) regression, which assumes homoskedasticity (constant variance). **Why not the other options:** - **B (Homoskedasticity)**: This would mean constant variance of errors, which is the ideal condition but not indicated here - **C (Perfect multicollinearity)**: This occurs when independent variables are perfectly correlated, which is a different issue - **D (Non-perfect multicollinearity)**: This refers to high but not perfect correlation between independent variables Without additional context about the specific portfolio data, heteroskedasticity is a common issue in financial time series data where volatility clusters occur.

Author: LeetQuiz Editorial Team