Financial Risk Manager Part 1
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Which of the following is least likely to be the method of handling data with heteroscedastic data?
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NNikitesh
A
Use of weighted least squares (WLS)
B
Ignoring the heteroskedasticity when approximating the parameters and then utilize the White covariance estimator in hypothesis tests.
C
Transforming the data in an attempt to remove heteroskedasticity.
D
None of the above
Explanation:
The correct answer is D because all three options A, B, and C are valid methods for handling heteroscedastic data:
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Weighted Least Squares (WLS) - A sophisticated method that applies weights to data to address heteroscedasticity by transforming the regression equation.
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Ignoring heteroskedasticity initially and using White covariance estimator - A common practical approach where parameters are estimated ignoring heteroscedasticity, but robust standard errors (White estimator) are used for hypothesis testing.
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Data transformation - Methods like log transformation or dividing variables to stabilize variance and remove heteroscedasticity patterns.
Since all three are legitimate methods for handling heteroscedastic data, 'None of the above' is the least likely to be a method, making it the correct answer.
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