We propose an extrapolative model of bubbles to explain the sharp rise in prices and volume observed in historical financial bubbles. The model generates a novel mechanism for volume: because of the interaction between extrapolative beliefs and disposition effects, investors are quick to not only buy assets with positive past returns but also sell them if good returns continue. Using account-level transaction data on the 2014–2015 Chinese stock market bubble, we test and confirm the model’s predictions about trading volume. We quantify the magnitude of the proposed mechanism and show that it can increase trading volume by another 30%.

We incorporate a time-varying negative relationship for implied volatility and underlying price into the delta hedging problem, where traders aim to minimize the variance of changes in the value of an option position by trading an appropriate amount of the underlying asset. We show that volatility-price elasticity is mean-reverting and embed predictions of the elasticity in a hedge ratio model that incorporates the negative volatility-price relationship. Our tests show that when applied to index options data, the proposed approach improves hedging performance over methods that rely solely on the long-run mean of the volatility-price relationship.

Existing research finds that investors’ returns vary with their wealth and level of sophistication. We bring a new perspective from the supply side by showing that return heterogeneity can be magnified as assets offered by the market become more complex. Using detailed account-level data, we examine the trading of B funds—complex, structured products in the Chinese market. During a 3-year market cycle, the return gap between the naive and sophisticated is an order-of-magnitude greater when trading B funds than when trading simple, non-structured funds. In an event study, we confirm that this disparity is driven by differences in investors’ understanding of product complexity.