Dynamic Asset Pricing Theory is a textbook for doctoral students and researchers on the theory of asset pricing and portfolio selection in multiperiod settings under uncertainty. The asset pricing results are based on the three increasingly restrictive assumptions: absence of arbitrage, single-agent optimaltiy, and equilibrium. These results are unified with two key concepts, state prices and martingales. Technicalities are given relatively little emphasis so as to draw connections between these concepts and to make plain the similarities between discrete and continuous-time models. For simplicity, all continuous-time models are based on Brownian motion. Applications include term structure models, derivative valuation and hedging methods, and dynamic programming algorithms for portfolio choice and optimal exercise of American options. Numerical methods covered include Monte Carlo simulation and finite-difference solvers for partial differential equations. Each chapter provides extensive problem exercises and notes to the literature. This second edition is substantially longer, while still retaining the consciseness for which the first edition was praised. All chapters from the first edition have been revised. Two new chapters have been added on term structure modeling and on derivative securities. References have been updated throughout. With this new edition, Dynamic Asset Pricing Theory remains the definitive textbook in the field.

This paper presents an econometric model to value latent information underlying corporate events. This model computes the market's inference of the value of latent information from the probability of an event, conditional on firm‐specific, preevent information. It provides a convenient framework for testing significance of preevent information variables, such as accounting attributes and lagged stock return. Simulations show that this model, when applied to both event and preevent period data, can decrease the incidence of bias in event studies. If restricted to only event period data, this model reduces to a truncated regression and does not perform as well as standard procedures.

This paper uses the betting market for professional basketball games to address the issue of unexplained asset price volatility. A pricing model is presented which identifies two components in point spreads for professional basketball games. Both components—the market's estimate of relative team abilities and an idiosyncratic factor—are essentially unobserved, but can be identified ex post. The structure of this market enables tests of competing hypotheses about point spread variation. The tests reject the hypothesis that variation in the two components represents irrelevant noise. The hypothesis that unobserved fundamentals account for this variation is consistent with the data.

Individual investors trade less agressively on any particular piece of information as more investors observe it. The trades of the new investors observing a piece of information “crowd out” some of the trades of the old investors who observe that same piece of information. This paper shows that when traders are risk averse, these crowding out effects lead the proportions of traders who choose to observe one signal versus another to differ from the proportions that maximize the informativeness of prices.

The effect of short selling on the composition and location of the efficient set has been analyzed in a variety of ways. However, the situation typically facing investors where the initial margin requirement is less than 100 percent and the riskfree interest rate that is paid on the short proceeds is less than the rate paid on initial margin has not previously been considered. The Elton‐Gruber‐Padberg algorithm (1976, 1978), subject to certain modifications, is shown here to be capable of identifying the efficient set under such conditions.