The Review of Asset Pricing Studies (the RAPS) is ending its third year as a publishing journal, and now is a good time to talk about how we are doing and to review the state of our new journal. We are doing well. You, our authors, are submitting high quality papers, and you, our referees, are writing high quality reviews. Some of the world’s leading scholars serve on our editorial board. Oxford University Press, our publisher, is producing great looking volumes and managing various web-based forums for our journal’s dissemination. The Society of Financial Studies (SFS), which owns three journals including the RAPS, has been very supportive as well. None of this surprises me. When I agreed to be the first Editor of the new journal I knew that I would be working with top quality people and organizations. But, there have been some surprises. One surprise is that the...

Multibeta asset pricing models are examined using proxies for economic state variables in a framework that exploits time-varying expected returns to estimate conditional betas. Examples include multiple consumption-beta models and models where asset returns proxy for the state variables. When the state variables are not specified, the tests indicate two or three time-varying expected risk premiums in the sample of quarterly asset returns. Conditional betas relative to consumption generate less striking evidence against the model than betas relative to asset returns, but both the consumption and the market variables fail to proxy for the state variables.

[We develop asset pricing models' implications for portfolio efficiency with conditioning information in the form of lagged instruments. A model identifies a portfolio that should be minimum-variance efficient with respect to the conditioning information. Our framework refines tests of portfolio efficiency by using the given conditioning information optimally. The optimal use of the lagged variables is economically important; by using the instruments optimally, we reject several efficiency hypotheses that are not otherwise rejected. The Sharpe ratios of a sample of hedge fund indexes appear consistent with the optimal use of conditioning information.]

Hansen and Jagannathan (1991) (hereafter HJ) derive restrictions on the volatility of stochastic discount factors that price a given set of returns. This article studies the sampling properties of HJ bounds that use conditioning information. One approach is to multiply the returns by the lagged variables. We also study optimized HJ bounds with conditioning information from Gallant, Hansen, and Tauchen (1990) and based on portfolios derived in Ferson and Siegel (2001). We document striking finite-sample biases in the HJ bounds, where the bounds reject asset-pricing models too often. We provide a useful bias correction. We also evaluate asymptotic standard errors for the bounds from Hansen, Heaton, and Luttmer (1995).

[We investigate predictability in national equity market returns, and its relation to global economic risks. We show how to consistently estimate the fraction of the predictable variation that is captured by an asset pricing model for the expected returns. We use a model in which conditional betas of the national equity markets depend on local information variables, while global risk premia depend on global variables. We examine single- and multiple-beta models, using monthly data for 1970 to 1989. The models capture much of the predictability for many countries. Most of this is related to time variation in the global risk premia.]

Recently, the finance literature has included empirical analysis of consumption in asset pricing models based on the cross-equation restrictions implied by optimality of a representative agent's consumption and investment plan. These studies have required some specification of an aggregate utility function, and power (constant relative risk aversion) utility has been predominant. The present paper extends this body of research by including models with constant absolute, as well as constant relative, risk aversion.

This paper provides an analysis of the predictable components of monthly common stock and bond portfolio returns. Most of the predictability is associated with sensitivity to economic variables in a rational asset pricing model with multiple betas. The stock market risk premimum is the most important for capturing predictable variation of the stock portfolios, while premiums associated with interest rate risks capture predictability of the bond returns. Time variation in the premium for beta risk is more important than changes in the betas.

Previous studies identify predetermined variables that predict stock and bond returns through time. This paper shows that loadings on the same variables provide significant cross‐sectional explanatory power for stock portfolio returns. The loadings are significant given the three factors advocated by Fama and French (1993) and the four factors of Elton, Gruber, and Blake (1995). The explanatory power of the loadings on lagged variables is robust to various portfolio grouping procedures and other considerations. The results carry implications for risk analysis, performance measurement, cost‐of‐capital calculations, and other applications.