Error analysis of a model order reduction framework for financial risk analysis

A parametric model order reduction (MOR) approach for simulating high-dimensional models arising in financial risk analysis is proposed on the basis of proper orthogonal decomposition (POD) to generate small approximations convection-diffusion reaction partial differential equations (PDE). The technique uses an adaptive greedy sampling based surrogate modeling efficiently locate most relevant training parameters, thus generating optimal reduced basis. best suitable procured such that total error less than a user-defined tolerance. three major errors considered are discretization associated with full obtained by discretizing PDE, error, and parameter error. developed analyzed, implemented, tested industrial data puttable steepener under two-factor Hull-White model. results illustrate provides significant speedup excellent accuracy over approach, demonstrating its potential applications historical or Monte Carlo Value-at-Risk calculations.