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  3. GARCH Models in Python

Exercise

Fixed rolling window forecast

Rolling-window forecasts are very popular for financial time series modeling. In this exercise, you will practice how to implement GARCH model forecasts with a fixed rolling window.

First define the window size inside .fit(), and perform the forecast with a for-loop. Note since the window size remains fixed, both the start and end points increment after each iteration.

The S&P 500 return series has been preloaded as sp_data, and a GARCH(1,1) model has been predefined in basic_gm. The start and end points of the initial sample window has been pre-defined in start_loc and end_loc respectively.

Instructions 1/3

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  • Define the fixed rolling window size by specifying first_obs = and last_obs = in the .fit() function.