Make forecast with GARCH models

Previously you have implemented a basic GARCH(1,1) model with the Python arch package. In this exercise, you will practice making a basic volatility forecast.

You will again use the historical returns of S&P 500 time series. First define and fit a GARCH(1,1) model with all available observations, then call .forecast() to make a prediction. By default it produces a 1-step ahead estimate. You can use horizon = n to specify longer forward periods.

The arch package has been preloaded for you.

This exercise is part of the course

GARCH Models in Python

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Specify a GARCH(1,1) model
basic_gm = ____(sp_data['Return'], p = 1, q = 1, 
                      mean = 'constant', vol = 'GARCH', dist = 'normal')
# Fit the model
gm_result = basic_gm.____()

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