Simulation on demand for pricing many securities

We develop a sequential experiment design procedure to construct multiple metamodels based on a single stochastic simulation model. We apply the procedure to approximate many securities´ prices as functions of a financial scenario. We propose a cross-validation method that adds design points and simulation effort at the design points to target all metamodels´ relative prediction errors. To improve the expected quality of the metamodels given randomness of the scenario that is an input to the simulation model, we also propose a way to choose design points so that the scenario is likely to fall inside their convex hull.