Regularization Effect on Model Calibration

28 Pages Posted: 1 Mar 2022

See all articles by Mesias Alfeus

Mesias Alfeus

Department of Statistics and Actuarial Science - Stellenbosch University

Xin-Jiang He

Zhejiang University of Technology

Song-Ping Zhu

University of Wollongong

Multiple version iconThere are 2 versions of this paper

Date Written: November 8, 2020

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.

Keywords: model calibration; regularization; option pricing model; out-of-sample forecast; global optimization.

Suggested Citation

Alfeus, Mesias and He, Xin-Jiang and Zhu, Song-Ping, Regularization Effect on Model Calibration (November 8, 2020). Journal of Risk, Vol. 24, No. 3, Available at SSRN: https://ssrn.com/abstract=4043891

Mesias Alfeus (Contact Author)

Department of Statistics and Actuarial Science - Stellenbosch University ( email )

Matieland
m
Stellenbosch, 7602
South Africa
0633236629 (Phone)
7405 (Fax)

Xin-Jiang He

Zhejiang University of Technology ( email )

China

Song-Ping Zhu

University of Wollongong ( email )

Northfield Ave.
Wollongong, NSW
Australia
61-2-42213807 (Phone)

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