On the computation of hedging strategies in affine GARCH models

40 Pages Posted: 5 Nov 2019 Last revised: 22 Dec 2020

See all articles by Maciej Augustyniak

Maciej Augustyniak

University of Montreal - Department of Mathematics and Statistics

Alexandru Badescu

University of Calgary

Date Written: December 21, 2020

Abstract

This paper discusses the computation of hedging strategies under affine Gaussian GARCH dynamics. The risk-minimization hedging strategy is derived in closed-form and related to minimum variance delta hedging. Several numerical experiments are conducted to investigate the accuracy and properties of the proposed hedging formula, as well as the convergence to its continuous-time counterpart based on the GARCH diffusion limit process. An empirical analysis with S&P 500 option data over 2001-2015 indicates that risk-minimization hedging with the affine Gaussian GARCH model outperforms benchmark delta hedges. Our study also reveals that the variance-dependent pricing kernel contributes to improving the hedging performance.

Keywords: affine GARCH models, mean-variance hedging, local risk-minimization, minimum variance hedge

JEL Classification: C58, G12, G13

Suggested Citation

Augustyniak, Maciej and Badescu, Alexandru, On the computation of hedging strategies in affine GARCH models (December 21, 2020). Available at SSRN: https://ssrn.com/abstract=3475245 or http://dx.doi.org/10.2139/ssrn.3475245

Maciej Augustyniak (Contact Author)

University of Montreal - Department of Mathematics and Statistics ( email )

C.P. 6128, succursale Centre-ville
Montreal, Quebec H3C 3J7
Canada

HOME PAGE: http://dms.umontreal.ca/

Alexandru Badescu

University of Calgary ( email )

University of Calgary
Calgary, Alberta
Canada

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