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Answer: OLS minimizes the sum of squared differences between the actual and estimated stock returns.
**Correct Answer: D** **Explanation:** D is correct. The OLS procedure is a method for estimating the unknown parameters in a linear regression model. The method minimizes the sum of squared differences between the actual, observed, returns and the returns estimated by the linear approximation. The smaller the sum of the squared differences between observed and estimated values, the better the estimated regression line fits the observed data points. A, B, and C are incorrect. None of these is the approach used for OLS estimators. Each of these approaches (regardless of which of the given variables is explanatory and which is dependent) would allow positive and negative differences to cancel each other out.
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A risk manager is estimating the sensitivity of a stock's return to the return on the S&P 500 Index. The manager performs this task using an ordinary least squares (OLS) regression. Which of the following descriptions of the OLS procedure is correct?
A
OLS minimizes the square of the sum of differences between the actual and estimated S&P 500 Index returns.
B
OLS minimizes the square of the sum of differences between the actual and estimated stock returns.
C
OLS minimizes the sum of differences between the actual and estimated squared S&P 500 Index returns.
D
OLS minimizes the sum of squared differences between the actual and estimated stock returns.
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