Fit a GARCH with skewed t-distribution
The default normal distribution assumption of the standardized residuals used in GARCH models are not representative of the real financial world. Fat tails and skewness are frequently observed in financial return data.
In this exercise, you will improve the GARCH model by using a skewed Student's t-distribution assumption. In addition, you will compare the model estimated volatility with that from a model with a normal distribution assumption by plotting them together.
A GARCH model with the default normal distribution assumption has been fitted for you, and its volatility estimation is saved in normal_vol.
This exercise is part of the course
GARCH Models in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Specify GARCH model assumptions
skewt_gm = arch_model(sp_data['Return'], p = 1, q = 1, mean = 'constant', vol = 'GARCH', ____ = '____')
# Fit the model
skewt_result = skewt_gm.____()