Robust information share measures with an application on the international crude oil markets
提出一种基于稳健向量误差修正模型的信息份额估计方法，在存在异常值时比传统方法更准确，并用2019年11月至2020年10月的WTI和布伦特高频原油价格数据验证了其有效性。
Abstract This paper proposes a robust estimation approach of the popular information share using a robust vector error correction model. Via simulation studies, we show that the proposed measures lead to more accurate estimates than the existing measures in the presence of outliers. The proposed measures are then investigated using high‐frequency crude oil prices data of the West Texas Intermediate and Brent over November 2019–October 2020. The results suggest that the abnormally large price movement in April 2020 may cause biased estimates of the ordinary measures, whereas the robust measures produce rather consistent results.