We can use the corr function to derive the correlation values between each column of values in the pandas DataFrame object, as in the following Python example:

>>> print log_returns.corr()
           EUROSTOXX    VSTOXX
EUROSTOXX   1.000000 -0.732545
VSTOXX     -0.732545  1.000000
[2 rows x 2 columns]

At -0.7325, the EURO STOXX 50 Index is negatively correlated with the STOXX. To help us better visualize this relationship, we can plot both the sets of the daily log return values as a scatter plot. The statsmodels.api module is used to obtain the ordinary least squares regression line between the scattered data:

>>> import statsmodels.api as sm
>>>
>>> log_returns.plot(figsize=(10,8),
...                  x="EUROSTOXX", 
...                  y="VSTOXX",
...                  kind='scatter')
>>>
>>> ols_fit = sm.OLS(log_returns['VSTOXX'].values, 
...             log_returns['EUROSTOXX'].values).fit()
&gt...