Garch Models, Volatility Forecasting, Financial Modeling, Time Series Analysis, Python.
preprocessing returns = data['log_return'].dropna() # Fit GARCH Model am = arch_model(returns, mean='Zero', vol='Garch', p=1, q=1) res = am.fit(disp='off') # Forecast volatility forecasts = res.forecast(horizon=5) # Print the forecasted volatility print(forecasts.mean.iloc[-1, :]) Output: h.1 0.0