Efficient techniques for the VaR decision problem
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  Value at Risk (VaR) is a popular measure of the financial risk of a
  portfolio. Monte Carlo techniques are often used to estimate this
  quantity. One of the main draw backs of these techniques is the
  computational cost involved. Under certain conditions, the 
  bottleneck is a matrix vector multiplication involved in the
  computation. In the VaR decision problem, we are interested not in
  the VaR itself, but in whether it exceeds a given threshold. In this
  talk, I will discuss linear algebra and computational geometry techniques 
  that yield significant increases in speed for this problem. Some of these
  ideas have potential applications to information retrieval too.