Measuring and Testing the Impact of News on Volatility
定义了新闻影响曲线，用于衡量新信息如何融入波动率估计，并比较了多种ARCH模型，发现Glosten-Jagannathan-Runkle模型表现最佳，EGARCH模型虽能捕捉大部分不对称性但条件方差波动性过高。
ABSTRACT This paper defines the news impact curve which measures how new information is incorporated into volatility estimates. Various new and existing ARCH models including a partially nonparametric one are compared and estimated with daily Japanese stock return data. New diagnostic tests are presented which emphasize the asymmetry of the volatility response to news. Our results suggest that the model by Glosten, Jagannathan, and Runkle is the best parametric model. The EGARCH also can capture most of the asymmetry; however, there is evidence that the variability of the conditional variance implied by the EGARCH is too high.