A Tale of Two Tails: A New Unique Information Share Measure Based on Copulas
提出一种基于Copulas的尾部信息份额度量，聚焦价格创新的尾部依赖，具有唯一可识别性，模拟显示在尾部依赖存在时能更准确估计市场对价格发现的贡献，并用高频原油期货数据验证。
Abstract I propose a novel measure of information share, termed tail information share (TIS), which focuses on modeling the tail dependence of price innovations using copulas. I discuss its detailed technical properties, including unique identifiability, estimation procedures, and statistical properties. The proposed TIS improves over two commonly used measures by providing meaningful economic rationale and unique identifiability. My simulation studies indicate that TIS can yield more accurate estimates of market-specific contributions to price discovery when tail dependence is present. Additionally, I demonstrate the asymptotic consistency and efficiency of TIS estimators. An empirical illustration is provided using a new dataset of high-frequency crude oil futures.