An efficient solution of an optimization problem in financial engineering

Abstract

A popular model for the pricing of financial derivatives is the stochastic alpha beta rho model. The model has the capabilities for fitting various volatility structures observed in options markets. An optimization problem needs to be solved for estimating parameters in the model. This work considers a computational partial differential equation approach for this calibration process. It is shown that the partial differential equation method outperforms methods based on analytical price approximations.