ARIMA-GARCH的python代码

Sure! Here is an example of ARIMA-GARCH model implemented in Python using the `arch` package:

import arch
from arch import arch_model
from statsmodels.tsa.arima_model import ARIMA

# Fit ARIMA model
arima_model = ARIMA(data, order=(p, d, q))
arima_result = arima_model.fit()

# Calculate residuals
residuals = arima_result.resid

# Fit GARCH model
garch_model = arch_model(residuals, vol='Garch', p=1, q=1)
garch_result = garch_model.fit()

# Print the model summary
print(garch_result.summary())

In this code, you first fit an ARIMA model to your data using the `ARIMA` function from the `statsmodels.tsa.arima_model` module. The `order` parameter specifies the order of the AR, I, and MA components.

Next, you calculate the residuals of the ARIMA model and use them as input to fit a GARCH model using the `arch_model` function from the `arch` package. The `vol` parameter specifies the volatility model to be used (in this case, GARCH), while `p` and `q` specify the order of the GARCH components.

Finally, you print the summary of the GARCH model using the `summary()` method of the `arch_result` object.

Note that you need to replace `data`, `p`, `d`, and `q` with your own data and desired values for the ARIMA and GARCH parameters. Make sure you have installed the `arch` package before running this code.