Financial Risk Manager Part 1

Explanation:

OLS (Ordinary Least Squares) regression requires several key assumptions to draw valid conclusions:

1. Zero conditional mean assumption: The expected value of the error term, conditional on the independent variable, is zero (E(εᵢ|Xᵢ) = 0). This ensures that the model is correctly specified and there are no omitted variables.

2. Independence of observations: The error terms are uncorrelated across observations. This is equivalent to saying that (X, Y) observations are independent and identically distributed (i.i.d.).

3. Normality assumption: The error term is normally distributed. This assumption is particularly important for hypothesis testing and constructing confidence intervals.

4. Homoscedasticity: The variance of the error term is constant across all observations.

5. Linearity: A linear relationship exists between the independent and dependent variables.

6. No perfect multicollinearity: The independent variables are not perfectly correlated with each other.

Since all three statements (A, B, and C) are correct assumptions required for OLS regression, option D ("All of the above") is the correct answer.

Additional context from the text:

• The text mentions that OLS requires three key assumptions: zero conditional mean, i.i.d. observations, and no large outliers.
• It also lists other assumptions including normality of error terms, linear relationship, and no omitted variables.
• All of these are standard assumptions for OLS regression to produce unbiased, consistent, and efficient estimates.