Parametric VaR

Value at Risk can also be computed parametrically using a method known as variance/co-variance VaR. This method allows you to simulate a range of possibilities based on historical return distribution properties rather than actual return values. You can calculate the parametric VaR(90) using:

# Import norm from scipy.stats
from scipy.stats import norm

# Calculate Parametric VaR
norm.ppf(confidence_level=0.10, mu, vol)

where mu and vol are the mean and volatility, respectively.

Returns data is available (in decimals) in the variable StockReturns.

Import norm from scipy.stats.
Calculate the mean and volatility of StockReturns and assign them to mu and vol, respectively.
Set the confidence_level for VaR(95).
Calculate VaR(95) using the norm.ppf() function, passing in the confidence level as the first parameter, with mu and vol as the second and third parameters.

Take Hint (-30 XP)

Exercise

Parametric VaR

Instructions