High-dimensional parameter calibration of interest rate model for the Korean insurance capital standard

We propose a method for calibrating high-dimensional parameters in the Hull-White one-factor model using market prices of swaptions, aimed at generating mark-to-market interest rate scenarios in the Korean insurance industry. Our approach integrates a trust region-based Bayesian optimization technique with a parameter space decomposition method to solve the calibration problem. Empirical studies demonstrate that our method achieves superior stability and effectiveness in calibrating high-dimensional parameters compared to conventional Bayesian optimization approaches.