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ARIMA-GARCH
ARIMA-GARCH is a hybrid model that combines ARIMA (AutoRegressive Integrated Moving Average) for modeling the mean of a time series and GARCH (Generalized Autoregressive Conditional Heteroskedasticity) for modeling the variance (volatility). This approach is especially useful for financial and economic time series with changing volatility.
Key Features
- Captures both mean and volatility dynamics
- Widely used in econometrics and finance
- Flexible for different types of time series data
Example Use
# Python (statsmodels, arch)
from statsmodels.tsa.arima.model import ARIMA
from arch import arch_model
# Fit ARIMA to the mean
arima = ARIMA(data, order=(1,1,1)).fit()
residuals = arima.resid
# Fit GARCH to the residuals
garch = arch_model(residuals, vol='Garch', p=1, q=1).fit()