Exercise

# Parameter estimation: Skewed Normal

In the previous exercise you found that fitting a Normal distribution to the investment bank portfolio data from 2005 - 2010 resulted in a poor fit according to the Anderson-Darling test.

You will test the data using the `skewtest()`

function from `scipy.stats`

. If the test result is statistically different from zero, then the data support a skewed distribution.

Now you'll parametrically estimate the 95% VaR of a loss distribution fit using `scipy.stats`

's `skewnorm`

**skewed Normal** distribution. This is a more general distribution than the Normal and allows losses to be non-symmetrically distributed. We might expect losses to be skewed during the crisis, when portfolio losses were more likely than gains.

Portfolio `losses`

for the 2007 - 2009 period are available.

Instructions

**100 XP**

- Import
`skewnorm`

and`skewtest`

from`scipy.stats`

. - Test for skewness in portfolio
`losses`

using`skewtest`

. The test indicates skewness if the result is statistically different from zero. - Fit the
`losses`

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

method. - Generate and display the 95% VaR estimate from the fitted distribution.