Regularization effect on model calibration
Publication Name
Journal of Risk
Abstract
As is well known, the centerpiece of model calibration is regularization, which plays an important role in transforming an ill-posed calibration problem into a stable and well-formulated one. This realm of research has not been explored empirically in much detail in the literature. The goal of this paper is to understand and give an answer to a question concerning pricing accuracy using the parameters resulting from a correctly posed calibration problem in comparison with those inferred from a relaxed calibration. Our empirical findings indicate that regularized calibration is only to be recommended when considering out-of-sample pricing for a long time horizon.
Open Access Status
This publication is not available as open access
Volume
24
Issue
3
First Page
27
Last Page
53
Funding Number
GB202103001
Funding Sponsor
Australian Research Council