We solve the portfolio problem of a long‐run investor when the term structure is Gaussian and when the investor has access to nominal bonds and stock. We apply our method to a three‐factor model that captures the failure of the expectations hypothesis. We extend this model to account for time‐varying expected inflation, and estimate the model with both inflation and term structure data. The estimates imply that the bond portfolio of a long‐run investor looks very different from the portfolio of a mean‐variance optimizer. In particular, time‐varying term premia generate large hedging demands for long‐term bonds.

This paper proposes a dynamic risk-based model capable of jointly explaining the term structure of interest rates, returns on the aggregate market, and the risk and return characteristics of value and growth stocks. Both the term structure of interest rates and returns on value and growth stocks convey information about how the representative investor values cash flows of different maturities. We model how the representative investor perceives risks of these cash flows by specifying a parsimonious stochastic discount factor for the economy. Shocks to dividend growth, the real interest rate, and expected inflation are priced, but shocks to the price of risk are not. Given reasonable assumptions for dividends and inflation, we show that the model can simultaneously account for the behavior of aggregate stock returns, an upward-sloping yield curve, the failure of the expectations hypothesis, and the poor performance of the capital asset pricing model.

Why do value stocks have higher average returns than growth stocks, despite having lower risk? Why do these stocks exhibit positive abnormal performance, while growth stocks exhibit negative abnormal performance? This paper offers a rare-event-based explanation that can also account for the high equity premium and volatility of the aggregate market. The model explains other puzzling aspects of the data, such as joint patterns in time-series predictablity of aggregate market and value and growth returns, long periods in which growth outperforms value, and the association between positive skewness and low realized returns.

This paper describes estimation methods, based on the generalized method of moments (GMM), applicable in settings where time series have different starting or ending dates. We introduce two estimators that are more efficient asymptotically than standard GMM. We apply these to estimating predictive regressions in international data and show that the use of the full sample affects inference for assets with data available over the full period as well as for assets with data available for a subset of the period. Monte Carlo experiments demonstrate that reductions hold for small-sample standard errors as well as asymptotic ones.

We propose a dynamic risk‐based model that captures the value premium. Firms are modeled as long‐lived assets distinguished by the timing of cash flows. The stochastic discount factor is specified so that shocks to aggregate dividends are priced, but shocks to the discount rate are not. The model implies that growth firms covary more with the discount rate than do value firms, which covary more with cash flows. When calibrated to explain aggregate stock market behavior, the model accounts for the observed value premium, the high Sharpe ratios on value firms, and the poor performance of the CAPM.

We investigate whether a model with time‐varying probability of economic disaster can explain prices of collateralized debt obligations. We focus on senior tranches of the CDX, an index of credit default swaps on investment grade firms. These assets do not incur losses until a large fraction of previously stable firms default, and thus are deep out‐of‐the money put options on the overall economy. When calibrated to consumption data and to the equity premium, the model explains the spreads on CDX tranches prior to and during the 2008 to 2009 crisis.

Aggregate stock prices, relative to virtually any indicator of fundamental value, soared to unprecedented levels in the 1990s. Even today, after the market declines since 2000, they remain well above historical norms. Why? We consider one particular explanation: a fall in macroeconomicrisk, or the volatility of the aggregate economy. Empirically, we find a strong correlation between low-frequency movements in macroeconomic volatility and low-frequency movements in the stock market. To model this phenomenon, we estimate a two-state regime switching model for the volatility and mean of consumption growth, and find evidence of a shift to substantially lower consumption volatility at the beginning of the 1990s. We then use these estimates from postwar data to calibrate a rational asset pricing model with regime switches in both the mean and standard deviation of consumption growth. Plausible parameterizations of the model are found to account for a significant portion of the run-up in asset valuation ratios observed in the late 1990s.

Recent research finds that the stocks that mutual fund managers buy outperform the stocks that they sell (e.g., Chen, Jegadeesh, and Wermers (2000)). We study the nature of this stock-picking ability. We construct measures of trading skill based on how the stocks held and traded by fund managers perform at subsequent corporate earnings announcements. This approach increases the power to detect skilled trading and sheds light on its source. We find that the average fund’s recent buys significantly outperform its recent sells around the next earnings announcement, and that this accounts for a disproportionate fraction of the total abnormal returns to fund trades estimated in prior work. We find that mutual fund trades also forecast earnings surprises. We conclude that mutual fund managers are able to trade profitably in part because they are able to forecast earnings-related fundamentals.