Without the assumption of conditional homoskedasticity, a general asymptotic distribution theory for the two-stage cross-sectional regression method shows that the standard errors produced by the Fama–MacBeth procedure do not necessarily overstate the precision of the risk premium estimates. When factors are misspecified, estimators for risk premiums can be biased, and the t-value of a premium may converge to infinity in probability even when the true premium is zero. However, when a beta-pricing model is misspecified, the t-values for firm characteristics generally converge to infinity in probability, which supports the use of firm characteristics in cross-sectional regressions for detecting model misspecification.

Most empirical studies of the static capital asset pricing model (CAPM) assume that betas remain constant over time and that the return on the value-weighted portfolio of all stocks is a proxy for the return on aggregate wealth. The general consensus is that the static CAPM is unable to explain satisfactorily the cross-section of average returns on stocks. The authors assume that the CAPM holds in a conditional sense, i.e., betas and the market risk premium vary over time. They include the return on human capital when measuring the return on aggregate wealth. The authors' specification performs well in explaining the cross-section of average returns.

[We show that a mutual fund's stock selection skill can be decomposed into additional components that include liquidity-absorbing impatient trading and liquidity provision. We find that past performance predicts future performance better among funds trading in stocks affected more by information events: Past winners earn a risk-adjusted after-fee excess return of 35 basis points per month in the future. Most of that superior performance comes from impatient trading. We also find that impatient trading is more important for growth-oriented funds, and liquidity provision is more important for younger income funds.]

In this article, the authors develop alternative ways to compare asset pricing models when it is understood that their implied stochastic discount factors do not price all portfolios correctly. Unlike comparisons based on chi square statistics associated with null hypotheses that models are correct, the authors' measures of model performance do not reward variability of discount factor proxies. One of their measures is designed to exploit fully the implications of arbitrage-free pricing of derivative claims. The authors demonstrate empirically the usefulness of their methods in assessing some alternative stochastic factor models that have been proposed in asset pricing literature.

We provide a rationale for the presence of points in mortgage loan contracts. Our analysis builds on two key features. First, insurance markets are unavailable for labor income. Second, the “due-on-sale” clause allows banks to offer loan contracts which partially insure against fluctuations in labor income. If explicit prepayment penalties are prohibited by law, points serve effectively as prepayment penalties. We also examine environments where such penalties are not prohibited and show that points will be used if interest rates cannot depend on the size of the loan.

This paper shows that bank runs can be modeled as an equilibrium phenomenon. We demonstrate that some aspects of the intuitive “story” that bank runs start with fears of insolvency of banks can be rigorously modeled. If individuals observe long “lines” at the bank, they correctly infer that there is a possibility that the bank is about to fail and precipitate a bank run. However, bank runs occur even when no one has any adverse information. Extra market constraints such as suspension of convertibility can prevent bank runs and result in superior allocations.

We show how to use security market data to restrict the admissible region for means and standard deviations of intertemporal marginal rates of substitution (IMRSs) of consumers. Our approach (i) is nonparametric and applies to a rich class of models of dynamic economies, (ii) characterizes the duality between the mean--standard deviation frontier for IMRSs and the familiear mean- standard deviation frontier for asset returns, and (iii) exploits the restriction that IMRSs are positive random variables. The region provides a convenient summary of the sense in which asset market data are anaomalous from the vantage point of intertemporal asset pricing theory.

Globalization has increasingly made it possible for labor in developing countries to augment labor in the developed world, without having to relocate, in ways not thought possible only a few decades ago. We argue that this large increase in the developed world’s effective labor supply, triggered by geo-political events and technological innovations, coupled with the inability of existing institutions in the US and developing nations themselves to cope with this shock, set the stage for the great recession. The financial crisis in the US was but the first acute symptom.

We find that on average, an announcement of rising unemployment is good news for stocks during economic expansions and bad news during economic contractions. Unemployment news bundles three types of primitive information relevant for valuing stocks: information about future interest rates, the equity risk premium, and corporate earnings and dividends. The nature of the information bundle, and hence the relative importance of the three effects, changes over time depending on the state of the economy. For stocks as a group, information about interest rates dominates during expansions and information about future corporate dividends dominates during contractions.

Knowledge of the one‐month interest rate is useful in forecasting the sign as well as the variance of the excess return on stocks. The services of a portfolio manager who makes use of the forecasting model to shift funds between bills and stocks would be worth an annual management fee of 2% of the value of the assets managed. During 1954:4 to 1986:12, the variance of monthly returns on the managed portfolio was about 60% of the variance of the returns on the value weighted index, whereas the average return was two basis points higher.