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.

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.

Exercise

Parameter estimation: Skewed Normal

Instructions