Volatility and Informativeness

We explore the equilibrium relation between price volatility and price informativeness in financial markets, with the ultimate goal of characterizing the type of inferences that can be drawn about price informativeness by observing price volatility. We identify two different channels (noise reduction and equilibrium learning) through which changes in price informativeness are associated with changes in price volatility. We show that when informativeness is sufficiently high (low) volatility and informativeness positively (negatively) comove in equilibrium for any change in primitives. In the context of our leading application, we provide conditions on primitives that guarantee that volatility and informativeness always comove positively or negatively. We use data on U.S. stocks between 1963 and 2017 to recover stock-specific primitives and find that most stocks lie in the region of the parameter space in which informativeness and volatility comove negatively.