This paper studies the relation between volatility and informativeness in financial markets. We identify two channels (noise-reduction and equilibrium-learning) that determine the volatility-informativeness relation. When informativeness is sufficiently high (low), volatility and informativeness positively (negatively) comove in equilibrium. We identify conditions on primitives that guarantee that volatility and informativeness comove positively or negatively. We introduce the comovement score, a statistic that measures the distance of a given asset to the positive/negative comovement regions. Empirically, comovement scores (i) have trended downwards over the last decades, (ii) are positively related to value and idiosyncratic volatility and negatively to size and institutional ownership.

本文研究了金融市场波动性和信息量之间的关系。我们确定了两个决定波动率-信息量关系的渠道（降噪和均衡学习）。当信息量足够高（低）时，波动性和信息量在均衡中呈正向（负向）移动。我们确定了保证波动性和信息量正向或负向移动的原语条件。我们引入了联动得分，这是一种衡量给定资产与正/负联动区域距离的统计数据。从经验上看，联动得分 (i) 在过去几十年呈下降趋势，(ii) 与价值和异质波动性呈正相关，与规模和机构所有权呈负相关。