Generalized autoregressive score model with high-frequency data for optimal futures hedging

This study compares the performance of hedged equity index portfolios constructed using either a generalized autoregressive score (GAS) or a realized GAS (GRAS) model. GAS models encompass popular models, and studies indicate that high-frequency data improve a model's forecasting ability. The in-sample estimation results demonstrate that the GRAS model has better explanatory power and more robust time-varying variance and dependence parameters when fat-tailed distributions are accounted for. The out-of-sample comparison confirms its superiority in reducing hedged portfolio variance and accruing economic benefits to highly risk-averse hedgers.