On the computation of hedging strategies in affine GARCH models
研究了仿射高斯GARCH模型下风险最小化对冲策略的闭式解，数值实验和S&P 500期权数据（2001-2015）表明该策略优于基准Delta对冲，且方差依赖的定价核有助于提升对冲表现。
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.