Previous authors have raised the concern that there could be serious survival bias in the observed U.S. equity premium. Contrary to conventional wisdom, we argue that the survival bias in the U.S. data is unlikely to be significant. To reach this conclusion, we introduce a general framework for modeling survival and derive a mathematical relationship between the ex ante survival probability and the average survival bias. This relationship reveals the fundamental difficulty facing the survival argument: High survival bias requires an ex ante probability of market failure, which seems unrealistically high given the history of world financial markets.

We study hedge fund performance evaluation under the stochastic discount factor framework of Farnsworth, Ferson, Jackson, and Todd (FFJT). To accommodate dynamic trading strategies and derivatives used by hedge funds, we extend FFJT’s approach by considering models with option and time-averaged risk factors and incorporating option returns in model estimation. A wide range of models yield similar conclusions on the performance of simulated long/short equity hedge funds. We apply these models to 2,315 actual long/short equity funds from the Lipper TASS database and find that a small portion of these funds can outperform the market.

We develop a specification test and a sequence of model selection procedures for non-nested, overlapping, and nested models based on the second Hansen-Jagannathan distance, which requires a good asset pricing model to not only have small pricing errors but also be arbitrage free. Our methods have reasonably good finite sample performances and are more powerful than existing ones in detecting misspecified models with small pricing errors but are not arbitrage-free and in differentiating models that have similar pricing errors of a given set of test assets. Using the Fama and French size and book-to-market portfolios, we reach dramatically different conclusions on model performances based on our approach and existing methods.