Exercise

# Parameter estimation: Normal

*Parameter estimation* is the strongest method of VaR estimation because it assumes that the loss distribution class is **known**. Parameters are estimated to fit data to this distribution, and statistical inference is then made.

In this exercise, you will estimate the 95% VaR from a Normal distribution fitted to the investment bank data from 2007 - 2009. You'll use `scipy.stats`

's `norm`

distribution, assuming that it's the most appropriate class of distribution.

Is a Normal distribution a good fit? You'll test this with the `scipy.stats.anderson`

Anderson-Darling test. If the test result is statistically different from zero, this indicates the data is *not* Normally distributed. You'll address this in the next exercise.

Portfolio `losses`

for the 2005 - 2010 period are available.

Instructions

**100 XP**

- Import
`norm`

and`anderson`

from`scipy.stats`

. - Fit the
`losses`

data to the Normal distribution using the`.fit()`

method, saving the distribution parameters to`params`

. - Generate and display the 95% VaR estimate from the fitted distribution.
- Test the null hypothesis of a Normal distribution on
`losses`

using the Anderson-Darling test`anderson()`

.