**Expected Shortfall**
I agree with RussianMike on dangerous V@R-measurement.

In fact V@R is NOT a risk measure, I recommend that you should estimate maximum probable loss in 1d keeping view in mind of normal distribution, by compute the

__expected shortfall__ also referred to as

__conditional-V@R.__ See perhaps this

post! Or

here.

If you're able to estimate V@R, your expected shortfall estimation will be easy.

If q is your given threshold, you'll compute V@R by solving Normdist(x < V@R) = q and afterwards estimate your expected shortfall

*E**S**q* =

*E*(

*x* |

*x* < V@R).

In the case of normal distribution:

(X = N(\mu , \sigma^{2}) \\ Value at Risk_{q} = \mu + Quantil_{1-q}\sigma \\\Rightarrow\\ ES_{q} = \mu - \frac{normpdf(Quantil_{1-q})}{q}\sigma)

where (Quantil_{1-q}) is the 1-q-quantil of the standard normal distribution and

normalpdf is the probability density function

(normalpdf(x) = \frac{1}{\sqrt{2\pi}}e^{-\frac{x^{2}}{2}} )