Title :
Multilevel Monte Carlo metamodeling
Author :
Rosenbaum, Imry ; Staum, Jeremy
Author_Institution :
Dept. of Ind. Eng. & Manage. Sci., Northwestern Univ., Evanston, IL, USA
Abstract :
Multilevel Monte Carlo (MLMC) methods have been used by the information-based complexity community in order to improve the computational efficiency of parametric integration. We extend this approach by relaxing the assumptions on differentiability of the simulation output. Relaxing the assumption on the differentiability of the simulation output makes the MLMC method more widely applicable to stochastic simulation metamodeling problems in industrial engineering. The proposed scheme uses a sequential experiment design which allocates effort unevenly among design points in order to increase its efficiency. The procedure´s efficiency is tested on an example of option pricing in the Black-Scholes model.
Keywords :
Monte Carlo methods; computational complexity; design of experiments; industrial engineering; modelling; Black-Scholes model; MLMC methods; design points; differentiability; industrial engineering; information-based complexity; multilevel Monte Carlo metamodeling; option pricing; parametric integration; sequential experiment design; simulation output; stochastic simulation metamodeling problems; Computational modeling; Function approximation; Metamodeling; Monte Carlo methods; Random variables; Vectors;
Conference_Titel :
Simulation Conference (WSC), 2013 Winter
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4799-2077-8
DOI :
10.1109/WSC.2013.6721446
