Security Markets With Price Limits: A Bayesian Approach

Several financial markets impose daily price limits on individual securities. Once a price limit is triggered, investors observe either the limit floor or ceiling, but cannot know with certainty what the true equilibrium price would have been in the absence of such limits. The price limits in most exchanges are typically based on a percentage change from the previous day's closing price, and can be expressed as return limits. We develop a Bayesian forecasting model in the presence of return limits, assuming that security returns are governed by identically and independently shifted-exponential random variables with an unknown parameter. The unique features of our Bayesian model are the derivations of the posterior and predictive densities. Several numerical predictions are generated and depicted graphically. Our main theoretical result with policy implications is that when return-limit regulations are tightened, the price-discovery process is impeded and investor's welfare is reduced.