The project aims to study learning and volatility in financial markets. We will develop a theoretical market microstructure model to analyze how informational inefficiencies can arise in financial markets even though traders (who have non speculative reasons to trade) are allowed to buy or sell any quantity of an asset (in a continuous action space). In this theoretical framework, we will also analyze the case in which the asset value can change over time (e.g., because of shocks to the economy). We will study how learning occurs in this economy with changing fundamentals and how learning affects price volatility. This will create a bridge between the theoretical literature on learning and the empirical literature on time varying volatility (e.g., ARCH and GARCH). After developing the theoretical analyses, we will test the predictions in experiments, and proceed to a structural estimation of our models. We will run both field and laboratory experiments. The structural estimation will use transaction data in order to shed light on the process of information aggregation and volatility in different markets (e.g., more or less speculative) and different conditions (tranquil times versus financial crises).