Exercise

# Calculate covariances for volatility

In MPT, we quantify risk via volatility. The math for calculating portfolio volatility is complex, and it requires daily returns covariances. We'll now loop through each month in the `returns_monthly`

DataFrame, and calculate the covariance of the daily returns.

With pandas datetime indices, we can access the month and year with `df.index.month`

and `df.index.year`

. We'll use this to create a mask for `returns_daily`

that gives us the daily returns for the current month and year in the loop. We then use the mask to subset the DataFrame like this: `df[mask]`

. This gets entries in the `returns_daily`

DataFrame which are in the current month and year in each cycle of the loop. Finally, we'll use pandas' `.cov()`

method to get the covariance of daily returns.

Instructions

**100 XP**

- Loop through the index of
`returns_monthly`

. - Create a mask for
`returns_daily`

which uses the current month and year from`returns_monthly`

, and matches this to the current month and year from`i`

in the loop. - Use the mask on
`returns_daily`

and calculate covariances using`.cov()`

.