Exercise

# VaR and risk exposure

Previously you computed the VaR and CVaR when losses were Normally distributed. Here you'll find the VaR using another common loss distribution, the **Student's t-distribution** (or **T**) contained in `scipy.stats`

.

You'll compute an *array* of 99% VaR measures from the **T** distribution (with 30 - 1 = 29 degrees of freedom), using 30-day rolling windows from investment bank portfolio `losses`

.

First you'll find the mean and standard deviation of each window, creating a list of `rolling_parameters`

. You'll use these to compute the 99% VaR *array* of measures.

Then you'll use this *array* to plot the risk exposure for a portfolio initially worth $100,000. Recall that risk exposure is the probability of loss (this is 1%) multiplied by the loss amount (this is the loss given by the 99% VaR).

Instructions

**100 XP**

- Import the Student's
`t`

-distribution from`scipy.stats`

. - Compute the 30-day window mean
`mu`

and standard deviation`sigma`

vectors from`losses`

, and place into`rolling_parameters`

. - Compute a Numpy
**array**of 99% VaR measures`VaR_99`

using`t.ppf()`

, from a list of**T**distributions using the elements of`rolling_parameters`

. - Compute and visualize the risk exposure associated with the
`VaR_99`

array.