This paper examines the hypothesis that the expected rate of return to speculation in the forward foreign exchange market is zero; that is, the logarithm of the forward exchange rate is the market's conditional expectation of the logarithm of the future spot rate. A new computationally tractable econometric methodology for examining restrictions on a k-step-ahead forecasting equation is employed. Using data sampled more finely than the forecast interval, we are able to reject the simple market efficiency hypothesis for exchange rates from the 1970s and the 1920s. For the modern experience, the tests are also inconsistent with several alternative hypotheses which typically characterize the relationship between spot and forward exchange rates.

We examine international stock return comovements using country-industry and country-style portfolios as the base portfolios. We first establish that parsimonious risk-based factor models capture the data covariance structure better than the popular Heston–Rouwenhorst (1994) model. We then establish the following stylized facts regarding stock return comovements. First, there is no evidence for an upward trend in return correlations, except for the European stock markets. Second, the increasing importance of industry factors relative to country factors was a short-lived phenomenon. Third, large growth stocks are more correlated across countries than are small value stocks, and the difference has increased over time.

Monetary models based on cash-in-advance constraints make strong predictions about the stochastic properties of endogeneous variables such as the velocity of circulation of money, the rate of inflation, and real and nominal interest rates. We develop numerical methods to understand these predictions because the models cannot be characterized analytically. We calibrate some cash-in-advance models using driving processes estimated from U. S. time-series data to generate model predictions that are compared to sample statistics. Formulations of the models that generate variability in velocity corresponding to the U.S. data typically fail along other dimensions.

This paper evaluates the specification errors of several empirical asset pricing models that have been developed as potential improvements on the CAPM. We use the methodology of Hansen and Jagannathan (J. Finance 51 (1997) 3), and the test assets are the 25 Fama-French (J. Financial Econom. 52 (1997) 557) equity portfolios sorted on size and book-to-market ratio, and the Treasury bill. We allow the parameters of each model's pricing kernel to fluctuate with the business cycle. While we cannot reject correct pricing for Campbell's (J. Political Econom. 104 (1996) 298) model, stability tests indicate that the parameters may not be stable. A robustness test also indicates that none of the models correctly price returns that are scaled by the term premium.

We investigate the expectations hypotheses of the term structure of interest rates and of the foreign exchange market using vector autoregressive methods for U.S. dollar, Deutsche mark, and British pound interest rates and exchange rates. We examine Wald, Lagrange multiplier, and distance metric tests by iterating on approximate solutions that require only matrix inversions. Bias‐corrected, constrained VARs provide Monte Carlo simulations. Wald tests grossly overreject the null, Lagrange multiplier tests slightly underreject, and distance metric tests overreject. A common interpretation emerges from the small sample statistics. The evidence against the expectations hypotheses is much less strong than under asymptotic inference.

The paper characterizes predictable components in excess rates of returns on major equity and foreign exchange markets using lagged excess returns, dividend yields, and forward premiums as instruments. Vector autoregressive techniques demonstrate one-step-ahead predictability and provide implied long-horizon statistics. We estimate latent variable models as constrained counterparts to the VARs. The predictability of returns is related to asset pricing models by examining the volatility bounds on intertemporal marginal rates of substitution.

The paper first characterizes the predictable components in excess rates of returns on major equity and foreign exchange markets using lagged excess returns, dividend yields, and forward premiums as instruments. Vector autoregressions (VARs) demonstrate one‐step‐ahead predictability and facilitate calculations of implied long‐horizon statistics, such as variance ratios. Estimation of latent variable models then subjects the VARs to constraints derived from dynamic asset pricing theories. Examination of volatility bounds on intertemporal marginal rates of substitution provides summary statistics that quantify the challenge facing dynamic asset pricing models.

This paper examines the hypothesis that the expected rate of return to speculation in the forward foreign exchange market is zero; that is, the logarithm of the forward exchange rate is the market's conditional expectation of the logarithm of the future spot rate. A new computationally tractable econometric methodology for examining restrictions on a k-step-ahead forecasting equation is employed. Using data sampled more finely than the forecast interval, we are able to reject the simple market efficiency hypothesis for exchange rates from the 1970s and the 1920s. For the modern experience, the tests are also inconsistent with several alternative hypotheses which typically characterize the relationship between spot and forward exchange rates.

Monetary models based on cash-in-advance constraints make strong predictions about the stochastic properties of endogeneous variables such as the velocity of circulation of money, the rate of inflation, and real and nominal interest rates. We develop numerical methods to understand these predictions because the models cannot be characterized analytically. We calibrate some cash-in-advance models using driving processes estimated from U. S. time-series data to generate model predictions that are compared to sample statistics. Formulations of the models that generate variability in velocity corresponding to the U.S. data typically fail along other dimensions.

We examine aggregate idiosyncratic volatility in 23 developed equity markets, measured using various methodologies. We find no evidence of upward trends after extending the sample to 2008. Instead, idiosyncratic volatility is well described by a stationary autoregressive process that occasionally switches into a higher-variance regime that has relatively short duration. We also document that idiosyncratic volatility is highly correlated across countries. Most of the time variation in idiosyncratic volatility can be attributed to variation in a growth opportunity proxy, total (U.S.) market volatility, and in most specifications, the variance premium, a business cycle sensitive risk indicator.