This paper surveys the field of asset pricing. The emphasis is on the interplay between theory and empirical work and on the trade‐off between risk and return. Modern research seeks to understand the behavior of the stochastic discount factor (SDF) that prices all assets in the economy. The behavior of the term structure of real interest rates restricts the conditional mean of the SDF, whereas patterns of risk premia restrict its conditional volatility and factor structure. Stylized facts about interest rates, aggregate stock prices, and cross‐sectional patterns in stock returns have stimulated new research on optimal portfolio choice, intertemporal equilibrium models, and behavioral finance.

Expectations theories of asset returns may be interpreted as stating either that risk premia are zero, or that they are constant through time. Under the former interpretation, different versions of the expectations theory of the term structure are inconsistent with one another, but I show that this does not necessarily carry over to the constant risk premium interpretation of the theory. Furthermore, I argue that differences among expectations theories are of 'second order" in a precise mathematical sense. I present an approximate linearized framework for analysis of the term structure in which these differences disappear, and I test its accuracy in practice using data from the CRSP government bond tapes.

Expectations theories of asset returns may be interpreted either as stating that risk premia are zero or that they are constant through time. Under the former interpretation, different versions of the expectations theory of the term structure are inconsistent with one another, but I show that this does not necessarily carry over to the constant risk premium interpretation of the theory. I present a general equilibrium example in which different types of risk premium are constant through time and dependent only on maturity. Furthermore, I argue that differences among expectations theories are second‐order effects of bond yield variability. I develop an approximate linearized framework for analysis of the term structure in which these differences disappear, and I test its accuracy in practice using data from the CRSP government bond tapes.

This paper uses an equilibrium multifactor model to interpret the cross-sectional pattern of postwar U.S. stock and bond returns. Priced factors include the return on a stock index, revisions in forecasts of future stock returns (to capture intertemporal hedging effects), and revisions in forecasts of future labor income growth (proxies for the return on human capital). Aggregate stock market risk is the main factor determining excess returns; but in the presence of human capital or stock market mean reversion, the coefficient of relative risk aversion is much higher than the price of stock market risk.

This paper uses an equilibrium multifactor model to interpret the cross-sectional pattern of postwar U.S. stock and bond returns. Priced factors include the return on a stock index, revisions in forecasts of future stock returns (to capture intertemporal hedging effects), and revisions in forecasts of future labor income growth (proxies for the return on human capital). Aggregate stock market risk is the main factor determining excess returns but, in the presence of human capital or stock market mean reversion, the coefficient of relative risk aversion is much higher than the price of stock market risk.

We show that reaching for yield—a tendency to take more risk when the real interest rate declines while the risk premium remains constant—results from imposing a sustainable spending constraint on an otherwise standard infinitely lived investor with power utility. When the interest rate is initially low, reaching for yield intensifies. The sustainable spending constraint also affects the response of risk-taking to a change in the risk premium, which can even change sign. In a variant of the model where the sustainable spending constraint is formulated in nominal terms, low inflation also encourages risk-taking.

It seems plausible that an increase in stock market volatility raises required stock returns, and thus lowers stock prices. We develop a formal model of this volatility feedback effect using a simple model of changing variance (a quadratic generalized autoregressive conditionally heteroskedastic, or QGARCH, model). Our model is asymmetric and helps to explain the negative skewness and excess kurtosis of U.S. monthly and daily stock returns over the period 1926–1988. We find that volatility feedback normally has little effect on returns, but it can be important during periods of high volatility.

[In this article we break assets' betas with common factors into components attributable to news about future cash flows, real interest rates, and excess returns. To achieve this decomposition, we use a vector autoregressive time-series model and an approximate log-linear present value relation. The betas of industry and size portfolios with the market are largely attributed to changing expected returns. Betas with inflation and industrial production reflect opposing cash flow and expected return effects. We also show how asset pricing theory restricts the expected excess return components of betas.]