arch_model constructs an ARCH-family volatility model from return data. It is useful when volatility clusters during crises and later mean-reverts toward earlier levels. section 3.2.2
A practical workflow is:
- Compute percentage returns, multiply them by 100 for more reliable optimizer convergence, and remove the initial NaN. section 3.2.2
- Construct arch_model(returns), fit it, and request a forecast horizon such as 63 trading days. section 3.2.2
- Read residual_variance forecasts and sum, annualize, and rescale them to obtain a volatility forecast. section 3.2.2
Listing 3.9 Using a GARCH model to forecast volatility
from arch import arch_model
AAPL_prices = yf.download('AAPL', start='2000-01-03', end='2022-05-24')['Adj
Close']
returns = 100 * AAPL_prices.pct_change().dropna() #1
horizon = 63 #2
am = arch_model(returns) #3
res = am.fit() #4
forecasts = res.forecast(horizon=horizon) #5
print(forecasts.residual_variance.iloc[-1,:]) #6
The complete AAPL example shows importing arch_model, preparing returns, fitting the model, and forecasting 63 horizons. section 3.2.2
