Generalized autoregressive score model with high‐frequency data for optimal futures hedging
比较了广义自回归得分模型和含高频数据的广义自回归得分模型在构建对冲股票指数组合时的表现，发现后者在降低组合方差和带来经济效益上更优。
Abstract 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.