We present a reformulation of nonlinear optimization problems with chance constraints that replaces a probabilistic constraint by a nonparametric value-at-risk estimator. The estimator smooths the historical VaR via a kernel, resulting in a better approximation of the quantile and reducing the number of spurios local minima. An optimization algorithm is proposed that permits the efficient treatment of joint chance constraints. Numerical experiments on large-scale examples are presented.