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. This paper surveys the field of asset pricing. The emphasis is on the interplay between theory and empirical work. Theorists develop models with testable predictions; empirical researchers document “puzzles”—stylized facts that fail to fit established theories—and this stimulates the development of new theories. Such a process is part of the normal development of any science. Asset pricing, like the rest of economics, faces the special challenge that data are generated naturally rather than experimentally, and so researchers cannot control the quantity of data or the random shocks that affect the data. A particularly interesting characteristic of the asset pricing field is that these random shocks are also the subject matter of the theory. As Campbell, Lo, and MacKinlay ~1997, Chap. 1, p. 3! put it: What distinguishes financial economics is the central role that uncertainty plays in both financial theory and its empirical implementation. The starting point for every financial model is the uncertainty facing investors, and the substance of every financial model involves the impact of uncertainty on the behavior of investors and, ultimately, on mar-* Department of Economics, Harvard University, Cambridge, Massachusetts

anonymous referees and seminar participants at Stanford University and the 1999 Econometric Society

We review extensive evidence about how psychological biases affect investor behavior and prices. Systematic mispricing probably causes substantial resource misallocation. We argue that limited attention and overconfidence cause investor credulity about the strategic incentives of informed market participants. However, individuals as political participants remain subject to the biases and self-interest they exhibit in private settings. Indeed, correcting contemporaneous market pricing errors is probably not government’s relative advantage. Government and private planners should establish rules ex ante to improve choices and efficiency, including disclosure, reporting, advertising, and default-option-setting regulations. Especially

This paper is a critical survey of models designed for pricing fixed income securities and their associated term structures of market yields. Our primary focus is on the interplay between the theoretical specification of dynamic term structure models and their empirical fit to historical changes in the shapes of yield curves. We begin by overviewing the dynamic term structure models that have been fit to treasury or swap yield curves and in which the risk factors follow diffusions, jump-diffusion, or have “switching regimes. ” Then the goodness-of-fits of these models are assessed relative to their abilities to: (i) match linear projections of changes in yields onto the slope of the yield curve; (ii) match the persistence of conditional volatilities, and the shapes of term structures of unconditional volatilities, of yields; and (iii) to reliably price caps, swaptions, and other fixed-income derivatives. For the case of defaultable securities we explore the relative fits to historical yield spreads. 1

We investigate the extent to which inflation expectations have been more firmly anchored in the United Kingdom—a country with an explicit inflation target—than in the United States—which has no such target—using the difference between far-ahead forward rates on nominal and inflationindexed bonds as a measure of compensation for expected inflation and inflation risk at long horizons. We show that far-ahead forward inflation compensation in the U.S. exhibits substantial volatility, especially at low frequencies, and displays a highly significant degree of sensitivity to economic news. Similar patterns are evident in the U.K. prior to 1997, when the Bank of England was not independent, but have been strikingly absent since the Bank of England gained independence in 1997. Our findings are further supported by comparisons of dispersion in longerrun inflation expectations of professional forecasters and by evidence from Sweden, another inflation targeting country with a relatively long history of inflation-indexed bonds. Our results support the view that an explicit and credible inflation target helps to anchor the private sector’s

Changes in nominal interest rates must be due to either movements in real interest rates, expected inflation, or the inflation risk premium. We develop a term structure model with regime switches, time-varying prices of risk, and inflation to identify these components of the nominal yield curve. We find that the unconditional real rate curve is fairly flat at 1.44%, but slightly humped. In one regime, the real term structure is steeply downward sloping. Real rates (nominal rates) are pro-cyclical (counter-cyclical) and inflation is negatively correlated with real rates. An inflation risk premium that increases with the horizon fully accounts for the generally upward sloping nominal term structure. We find that expected inflation drives about 80 % of the variation of nominal yields at both short and long maturities, but during normal times, all of the

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This article complements the structural New-Keynesian macro framework with a no-arbitrage term structure model. Whereas our methodology is general, we focus on an extended macro-model with an unobservable time-varying markup and stochastic risk aversion. Term structure information helps to identify the dynamics of the observed and unobserved variables and the structural parameters. Moreover, the model yields a tractable linear system estimable by maximum likelihood or GMM. Whereas the VAR representation is simple when including term structure information, the reduced-form model for the observed macro variables is more complex. Relative to the term structure literature, we create an affine term structure model where all factors have an economic meaning and obey New-Keynesian structural relations. Our estimates yield a large Phillips curve parameter and a sensible curvature parameter for the utility function, making monetary policy quite effective. In the term structure, observable macro factors explain more of the variation of long yields compared with short yields. The unobservable factors contribute primarily to the dynamics of the slope and curvature factors in the term structure.

The term structure of interest rates, i.e., the yield curve, has long been of interest to monetary policymakers and their advisers. The transmission of monetary policy is conventionally viewed as running from shortterm interest rates managed by central banks to longer-term rates that influence aggregate demand. A central bank’s leverage over longer-term rates comes from the fact that the market determines these as the average expected level of short rates over the relevant horizon (abstracting from a term premium and default risk). Working in the other direction, the long bond rate contains a premium for expected inflation and, thus, serves as an indicator of the credibility of a central bank’s commitment to low inflation. 1 Different theoretical perspectives support the two above-mentioned uses of the term structure for monetary policy: John Hicks’s (1939) expectations theory of the term structure supports the first, and Irving Fisher’s (1896) decomposition of nominal bond rates into expected inflation and an expected real return supports the second. 2 The two views are compatible in principle, although reconciling them creates difficulties of interpretation in practice. For example, does a steepening yield curve indicate a loss of confidence in the central bank’s commitment to low inflation, or does it indicate that markets expect tighter

Using a new Bayesian method for the analysis of diffusion processes, this article finds that the nonlinear drift in interest rates found in a number of previous studies can be confirmed only under prior distributions that are best described as informative. The assumption of stationarity, which is common in the literature, represents a nontrivial prior belief about the shape of the drift function. This belief and the use of ``flat'' priors contribute strongly to the finding of nonlinear mean reversion. Implementation of an approximate Jeffreys prior results in virtually no evidence for mean reversion in interest rates unless stationarity is assumed. Finally, the article documents that nonlinear drift is primarily a feature of daily rather than monthly data, and that these data contain a transitory element that is not reflected in the volatility of longermaturity yields.