Financial Risk Manager Part 1
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Joel Matip, FRM, is running a regression model to forecast in-sample data. He's worried about data mining and over-fitting the data. The criterion that provides the highest penalty factor based on degrees of freedom is the:
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NNikitesh
A
Schwarz information criterion
B
Akaike information criterion
C
Unbiased mean squared error
D
Mean squared error
Explanation:
The Schwarz information criterion (SIC) provides the highest penalty factor based on degrees of freedom among the options.
Key points:
- MSE (Mean Squared Error) does not penalize the regression model based on the increased number of parameters (k).
- Unbiased MSE uses a penalty factor of T/(T-k) where T is sample size and k is number of parameters.
- Akaike information criterion (AIC) uses a penalty factor of e^(2k/T).
- Schwarz information criterion (SIC) uses a penalty factor of T^(k/T).
Among these penalty factors, SIC has the highest penalty for additional parameters, making it the most conservative criterion that helps prevent overfitting and data mining by strongly penalizing model complexity.
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