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Mean squared prediction errors

The GJR GARCH model is a generalization of the GARCH model. It should thus lead to a better fit in terms of lower Mean Squared Errors (MSE). Let's verify this on the Microsoft returns msftret for which garchfit corresponds to the estimation with the standard GARCH(1,1) model, while gjrfit is when the GJR model is used. Remember that you can compute the vector with prediction errors \(e\) for the mean using the residuals() method. The prediction error for the variance equals the difference between \(e^2\) and the predicted GARCH variance.

Questo esercizio fa parte del corso

GARCH Models in R

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Istruzioni dell'esercizio

  • Compute the vector with prediction errors for the means using the residuals() method.
  • Complete the code for calculating the MSE for garchfit estimation output.
  • Compute the MSE for the gjrfit estimation output.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Compute prediction errors
garcherrors <- ___(garchfit)
gjrerrors  <- ___(gjrfit)

# Compute MSE for variance prediction of garchfit model
___((___(garchfit)___ - garcherrors^2)___)

# Compute MSE for variance prediction of gjrfit model
___
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