Ljung-Box test
Another powerful tool to check autocorrelations in the data is the Ljung-Box test. In this exercise, you will practice detecting autocorrelation in the standardized residuals by performing a Ljung-Box test.
The null hypothesis of Ljung-Box test is: the data is independently distributed. If the p-value is larger than the specified significance level, the null hypothesis cannot be rejected. In other words, there is no clear sign of autocorrelations and the model is valid.
You will use the same GARCH model as the previous exercise. Its standardized residuals are saved in std_resid.
Questo esercizio fa parte del corso
GARCH Models in Python
Istruzioni dell'esercizio
- Import the module needed for Ljung-Box tests from the
statsmodelspackage. - Perform a Ljung-Box test up to lag 10, and save the result in
lb_test. - Print and review p-values from the Ljung-Box test result.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Import the Python module
from statsmodels.stats.diagnostic import ____
# Perform the Ljung-Box test
lb_test = ____(std_resid , ____ = ____, return_df = True)
# Print the p-values
print('P-values are: ', ____.iloc[0,1])