Model Calibration: Global Optimizer vs. Neural Network

Abstract

Calibration of financial models can have more than one local minima present, requiring the use of global optimization techniques to properly calibrate them. In general, calibrating with a global optimizer will be a slow operation. An artificial neural network, properly trained, can replicate the same behaviour, providing the calibrated parameters in a fraction of the time. In the following, a simulated annealing global optimizer is used to calibrate a two-factor Hull-White model. A neural network is then trained to replicate it, and back-tested out-of-sample with good results.