Numerical Methods for Fitting and Simulating Autoregressive-To-Anything Processes
INFORMS Journal on Computing
Design and Analysis of Simulation Experiments
Design and Analysis of Simulation Experiments
Stochastic Kriging for Simulation Metamodeling
Operations Research
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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.