We study how heterogeneous beliefs affect returns and examine whether they are a priced factor in traditional asset pricing models. To accomplish this task, we suggest new empirical measures based on the disagreement among analysts about expected earnings (short-term and long-term) and show they are good proxies. We first establish that the heterogeneity of beliefs matters for asset pricing and then turn our attention to estimating a structural model in which we use the forecasts of financial analysts to proxy for agents' beliefs. Finally, we investigate whether the amount of heterogeneity in analysts' forecasts can help explain asset pricing puzzles.

Many continuous-time term structure of interest rate models assume a factor structure where the drift and volatility functions are affine functions of the state-variable process. These models involve very specific parametric choices of factors and functional specifications of the drift and volatility. Moreover, under the affine term structure restrictions not all factors necessarily affect interest rates at all maturities simultaneously. This class of so-called affine models covers a wide variety of existing empirical as well as theoretical models in the literature. In this paper we take a very agnostic approach to the specification of these diffusion functions and test implications of the affine term structure restrictions. We do not test a specific model among the class of affine models per se. Instead, the affine term structure restrictions we test are based on the derivatives of the responses of interest rates to the factors. We also test how many and which factors affect a particular rate. These tests are conducted within a framework which models interest rates as functions of “fundamental” factors, and the responses of interest rates to these factors are estimated with nonparametric methods. We consider two sets of factors, one based on key macroeconomic variables, and one based on interest rate spreads. In general, despite their common use we find that the empirical evidence does not support the restrictions imposed by affine models. Besides testing the affine structure restrictions we also uncover a set of fundamental factors which appear remarkably robust in explaining interest rate dynamics at the long and short maturities we consider.

We use option prices to estimate ex ante higher moments of the underlying individual securities’ risk‐neutral returns distribution. We find that individual securities’ risk‐neutral volatility, skewness, and kurtosis are strongly related to future returns. Specifically, we find a negative (positive) relation between ex ante volatility (kurtosis) and subsequent returns in the cross‐section, and more ex ante negatively (positively) skewed returns yield subsequent higher (lower) returns. We analyze the extent to which these returns relations represent compensation for risk and find evidence that, even after controlling for differences in co‐moments, individual securities’ skewness matters.

A previous literature has documented that bond returns are predicted by macroeconomic information not contained in yields contemporaneously. That literature has mostly relied on final revised, rather than real time macroeconomic data. We show that the use of real time data substantially reduces the predictive power of macro variables for future bond returns as well as the implied countercyclicality of term premiums. We discuss potential interpretations of our results. Received January 26, 2014; editorial decision June 16, 2017 by Editor Geert Bekaert.

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We study asset pricing in economies featuring both risk and uncertainty. In our empirical analysis, we measure risk via return volatility and uncertainty via the degree of disagreement of professional forecasters, attributing different weights to each forecaster. We empirically model the typical risk-return trade-off and augment these models with our measure of uncertainty. We find stronger empirical evidence for an uncertainty-return trade-off than for the traditional risk-return trade-off. Finally, we investigate the performance of a two-factor model with risk and uncertainty in the cross section.

We document that the first and third cross-sectional moments of the distribution of GDP growth rates made by professional forecasters can predict equity excess returns, a finding that is robust to controlling for a large set of well-established predictive factors. We show that introducing time-varying skewness in the distribution of expected growth prospects in an otherwise standard endowment economy can substantially increase the model-implied equity Sharpe ratios, and produce a large amount of fluctuation in equity risk premiums.

We study how heterogeneous beliefs affect returns and examine whether they are a priced factor in traditional asset pricing models. To accomplish this task, we suggest new empirical measures based on the disagreement among analysts about expected earnings (short-term and long-term) and show they are good proxies. We first establish that the heterogeneity of beliefs matters for asset pricing and then turn our attention to estimating a structural model in which we use the forecasts of financial analysts to proxy for agents’ beliefs. Finally, we investigate whether the amount of heterogeneity in analysts’ forecasts can help explain asset pricing puzzles.

We propose a quantile‐based measure of conditional skewness, particularly suitable for handling recalcitrant emerging market (EM) returns. The skewness of international stock market returns varies significantly across countries over time, and persists at long horizons. In EMs, skewness is mostly positive and idiosyncratic, and significantly relates to a country's financial and trade openness and balance of payments. In an international portfolio setting, return asymmetry leads to sizeable certainty‐equivalent gains and increases the weight on emerging countries to about 30%. Investing in EMs seems to be about expectations of a higher upside than downside, consistent with recent theories.