On the computation of hedging strategies in affine GARCH models

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.

Automated summary

This paper explores the computation of hedging strategies under affine Gaussian GARCH dynamics, highlighting the superior performance of risk-minimization hedging with the affine Gaussian GARCH model. Through empirical analysis, the study shows that variance-dependent pricing kernel plays a crucial role in improving hedging efficiency.