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I asked this on stats.stackexchange but I realized this might be a better place to ask this question.

I am new to finance and volatility forecasting and am trying to understand how garch model works. Even though there are many tutorials on how to use arch_model from Python, none of them gives explanations of what happens behind the scenes.

After getting data

start = datetime(2010, 1, 1)
end = datetime(2021, 3, 31)
df = web.DataReader(['sp500'], 'fred', start, end)

We can simply compute returns and fit the model with

df['pct_change'] = df['sp500'].pct_change().dropna()
returns = df['pct_change'] * 100
am = arch.arch_model(returns)
res = am.fit()
res.params

Parameters I get are

mu          0.083702
omega       0.041945
alpha[1]    0.212129
beta[1]     0.754365

Now getting forecast of the volatility of the next day using all of the days before is:

vol_forecast = 0.01 * np.sqrt(res.params['omega'] + res.params['alpha[1]'] * res.resid**2 + res.conditional_volatility**2*res.params['beta[1]'])

May questions are the following:

  1. How can we compute residuals and conditional volatility ourselves, i.e. without resid and conditional_volatility from arch_model, only using data?

  2. How do we evaluate those models? We can get AIC and BIC from model summary but my question is rather if I want to do backtesting step by step and I forecast following 7 days (in this case 1st to 7th of April) how can I actually see how good my model performed in those seven days? If the answer is to use some of those information criteria which would be the best and how can I compute it by hand? In other words if I only have 7 numbers which are predictors of volatility in the next 7 days and 7 returns, how should I proceed?

  1. And lastly does the number I will get in forecast, say 1.4% means only that tomorrow's price of stock will 68% of time differ by 1.4% from today's price.

It would also be helpful to get any reading on this topic including building garch from scratch in Python.

Every answer is much appreciated! Thanks in advance!

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