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.

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.

The use of predetermined variables to represent public information and time-variation has produced new insights about asset pricing models but the literature on mutual fund performance has not exploited these insights. This paper advocates conditional performance evaluation in which the relevant expectations are conditioned on public information variables. The authors modify several classical performance measures to this end and find that the predetermined variables are both statistically and economically significant. Conditioning on public information controls for biases in traditional market timing models and makes the average performance of the mutual funds in the authors' sample look better.

Most of the evidence on consumption-based asset pricing is based on seasonally adjusted consumption data. The consumption-based models have not worked well for explaining asset returns, but with seasonally adjusted data there are reasons to expect spurious rejections of the models. This paper examines asset pricing models using not seasonally adjusted aggregate consumption data. The authors find evidence against models with time-separable preferences, even when the models incorporate seasonality and allow seasonal heteroskedasticity. A model that uses not seasonally adjusted consumption data and nonseparable preferences with seasonal effects works better according to several criteria. The parameter estimates imply a form of seasonal habit persistence in aggregate consumption expenditures.

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.