  We propose a probabilistic numerical algorithm to solve Backward Stochastic
Differential Equations (BSDEs) with nonnegative jumps, a class of BSDEs
introduced in [9] for representing fully nonlinear HJB equations. In
particular, this allows us to numerically solve stochastic control problems
with controlled volatility, possibly degenerate. Our backward scheme, based on
least-squares regressions, takes advantage of high-dimensional properties of
Monte-Carlo methods, and also provides a parametric estimate in feedback form
for the optimal control. A partial analysis of the error of the scheme is
provided, as well as numerical tests on the problem of superreplication of
option with uncertain volatilities and/or correlations, including a detailed
comparison with the numerical results from the alternative scheme proposed in
[7].
