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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.
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