Robust calibration of financial models using Bayesian estimators

Alok Gupta and Christoph Reisinger

Abstract

ABSTRACT

The authors consider a general calibration problem for derivative pricing models, which they reformulate into a Bayesian framework to attain posterior distributions for model parameters. They then show how the posterior distribution can be used to estimate prices for exotic options. They apply the procedure to a discrete local volatility model and work in great detail through numerical examples to clarify the construction of Bayesian estimators and their robustness to the model specification, number of calibration products, noisy data and misspecification of the prior.