title: Problems with Arch and GARCH models in python
body: <p>Im am trying to make a research in a Apple data in order to have more information about volatility and variance of the dataset. My idea was to use Garch and Arch models. However, when make a prediction with the trained part, the results have no logic.</p>
  <pre><code>from arch import arch_model
  n_test = 100
  train, test = AAPL[:-n_test], AAPL[-n_test:]
  
  model = arch_model(train, mean='Zero', vol='ARCH', p = 20)
  model_fit = model.fit()
  </code></pre>
  <p>I fit the model with the ARCH model, and when i do the prediction</p>
  <pre><code>import matplotlib.pyplot as plt
  yhat =model_fit.forecast(horizon=n_test)
  
  var = [i*0.01 for i in range(0,100)]
  plt.plot(var[-n_test:])
  
  plt.plot(yhat.variance.values[-1, :])
  plt.show()
  </code></pre>
  <p><a href="https://i.stack.imgur.com/KlaI5.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/KlaI5.png" alt="Plot of the test data and prediction" /></a></p>