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Specify and taste the GARCH model flavors

In the next chapters, you will see that GARCH models come in many flavors. You thus need to start off by specifying the mean model, the variance model and the error distribution that you want to use. The best model to use is application-specific. A realistic GARCH analysis thus involves specifying, estimating and testing various GARCH models.

In R, this is simple thanks to the rugarch package of Alexios Ghalanos. This package has already been loaded for you. You will apply it to analyze the daily returns in sp500ret.

Este ejercicio forma parte del curso

GARCH Models in R

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Instrucciones del ejercicio

  • Use ugarchspec() to specify that you want to estimate a standard GARCH(1,1) model with constant mean and a normal distribution for the prediction errors.
  • Use ugarchfit() to estimate the model by maximum likelihood.
  • Use the method sigma() to retrieve the estimated volatilities.
  • Plot the volatility predictions for 2017.

Ejercicio interactivo práctico

Prueba este ejercicio completando el código de muestra.

# Specify a standard GARCH model with constant mean
garchspec <- ___(mean.model = list(armaOrder = ___),
                 variance.model = list(model = "___"), 
                 distribution.model = "___")

# Estimate the model
garchfit <- ___(data = ___, spec = ___)

# Use the method sigma to retrieve the estimated volatilities 
garchvol <- ___ 

# Plot the volatility for 2017
___(___["2017"])
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