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.
Diese Übung ist Teil des Kurses
GARCH Models in Python
Interaktive Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
for i in range(30):
# Specify fixed rolling window size for model fitting
gm_result = basic_gm.fit(first_obs = i + ____,
last_obs = i + ____, update_freq = 5)