Quadratic hedging schemes for non-Gaussian GARCH models

TL;DR

This paper develops and compares quadratic hedging strategies for non-Gaussian GARCH models, extending existing methods to more accurately hedge options in complex, non-Gaussian financial environments.

Contribution

It introduces new local risk minimization and variance hedge schemes based on an extended Girsanov principle for non-Gaussian GARCH models, addressing measure construction issues.

Findings

Numerical analysis on S&P 500 options shows improved hedging performance.

Hedging strategies are robust to different risk-neutral measures.

The methods effectively link discrete and continuous GARCH models.

Abstract

We propose different schemes for option hedging when asset returns are modeled using a general class of GARCH models. More specifically, we implement local risk minimization and a minimum variance hedge approximation based on an extended Girsanov principle that generalizes Duan's (1995) delta hedge. Since the minimal martingale measure fails to produce a probability measure in this setting, we construct local risk minimization hedging strategies with respect to a pricing kernel. These approaches are investigated in the context of non-Gaussian driven models. Furthermore, we analyze these methods for non-Gaussian GARCH diffusion limit processes and link them to the corresponding discrete time counterparts. A detailed numerical analysis based on S&P 500 European Call options is provided to assess the empirical performance of the proposed schemes. We also test the sensitivity of the hedging strategies with respect to the risk neutral measure used by recomputing some of our results with an exponential affine pricing kernel.