Python for Finance Cookbook

How it works...

In Steps 2 and Step 3, we downloaded the daily stock prices of Google, Microsoft, and Apple. Then, we calculated simple returns and multiplied them by 100 to avoid encountering convergence errors.

In Step 5, we defined empty lists for storing elements required at later stages: GARCH coefficients, conditional volatilities, standardized residuals, and the models themselves (used for forecasting). In Step 6, we iterated over the columns of the DataFrame containing the stock returns and fitted a univariate GARCH model to each of the series. We stored the results in the predefined lists. Then, we wrangled the data in order to have objects such as residuals in DataFrames, to make work with them and visual inspection easier.

In ...