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.
Este ejercicio forma parte del curso
Machine Learning for Finance in Python
Instrucciones del ejercicio
- Loop through the index of
returns_monthly. - Create a mask for
returns_dailywhich uses the current month and year fromreturns_monthly, and matches this to the current month and year fromiin the loop. - Use the mask on
returns_dailyand calculate covariances using.cov().
Ejercicio interactivo práctico
Prueba este ejercicio y completa el código de muestra.
# Daily covariance of stocks (for each monthly period)
covariances = {}
rtd_idx = returns_daily.index
for i in returns_monthly.____:
# Mask daily returns for each month and year, and calculate covariance
mask = (rtd_idx.month == i.month) & (rtd_idx.____ == i.____)
# Use the mask to get daily returns for the current month and year of monthy returns index
covariances[i] = returns_daily[____].cov()
print(covariances[i])