  This paper covers a massive acceleration of Monte-Carlo based pricing method
for financial products and financial derivatives. The method is applicable in
risk management settings, where a financial product has to be priced under a
number of potential future scenarios. Instead of starting a separate nested
Monte Carlo simulation for each scenario under consideration, the new method
covers the utilization of very few representative nested simulations and
estimating the product prices at each scenario by a smoothing method based on
the state-space. This smoothing technique can be e.g. non-parametric regression
or kernel smoothing.
