  Adaptive importance sampling techniques are widely known for the Gaussian
setting of Brownian driven diffusions. In this work, we want to extend them to
jump processes. Our approach relies on a change of the jump intensity combined
with the standard exponential tilting for the Brownian motion. The free
parameters of our framework are optimized using sample average approximation
techniques. We illustrate the efficiency of our method on the valuation of
financial derivatives in several jump models.
