This article shows how rational asset pricing models restrict the regression-based criteria commonly used to measure return predictability. Specifically, it invokes no arbitrage arguments to show that the intercept, slope coefficients, and R-squared in predictive regressions must take specific values. These restrictions provide a way to directly assess whether the predictability uncovered using regression analysis is consistent with rational pricing. Empirical tests reveal that the returns on the CRSP size deciles are too predictable to be compatible with a number of well-known pricing models. However, the overall pattern of predictability across these portfolios is reasonably consistent with what we would expect under circumstances where predictability is rational.
U.S. stock portfolios sorted on size, momentum, transaction costs, M/B, I/A and ROA ratios, and industry classification show considerable levels and variation of return predictability, inconsistent with asset pricing models. This means that a predictable risk premium is not equal to compensation for systematic risk as implied by asset pricing theory (Kirby 1998). We show that introducing market frictions relaxes these asset pricing moments from a strict equality to a range. Empirically, it is not short sales constraints but transaction costs (below 35 basis points) that help to reconcile the observed predictability with the Fama-French-Carhart four-factor model and the Chen-Novy-Marx-Zhang three factor model, and partly with the Durable Consumption model. Across the sorts, predictability in industry returns can be reconciled with all models considered with only 25 basis points transaction costs, whereas for momentum and ROA portfolios up to 115 basis points are needed.
An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
Two broad classes of consumption dynamics - long-run risks and rare disasters - have proven successful in explaining the equity premium puzzle when used in conjunction with recursive preference. We show that bounds a-la Gallant, Hansen and Tauchen (1990) that restrict the volatility of the Stochastic Discount Factor by conditioning on a set of return predictors constitute a useful tool to discriminate between these alternative dynamics. In particular we document that models that rely on rare disasters meet comfortably the bounds independently of the forecasting horizon and the asset classes used to construct the bounds. However, the specific nature of disasters is a relevant characteristic at the 1-year horizon: disasters that unfold over multiple years are more successful in meeting the predictors-based bounds than one-period disasters. Instead, over a longer, 5-year horizon, the sole presence of disasters - even if one-period and permanent - is sufficient for the model to satisfy the bounds. Finally, the bounds point to multiple volatility components in consumption as a promising dimension for long-run risks models.
We use the evidence on predictability of returns at different horizons to discriminate among competing asset pricing models. Specifically, we employ predictors-based variance bounds, i.e. bounds on the variance of the Stochastic Discount Factors (SDFs) that price a given set of returns conditional on the information contained in a vector of return predictors. We show that return predictability delivers variance bounds that are much tighter than the classical, unconditional Hansen and Jagannathan (1991) bounds. We use the predictors-based bounds to discriminate among three leading classes of asset pricing models: rare disasters, long-run risks and external habit. We find that the rare disasters model of Nakamura, Steinsson, Barro, and Ursua (2013) is the best performer since it satisfies rather comfortably the predictors-based bounds at all horizons. As for long-run risks, while the classical version of Bansal and Yaron (2004) is the model most challenged by the introduction of conditioning information since it struggles to meet the bounds at all horizons, the more general version of Schorfheide, Song, and Yaron (2016), which accounts for multiple volatility components, satisfies the 1- and 5-year bounds as long as the set of test assets includes only equities and T-Bills. The Campbell and Cochrane (1999) habit model lies somehow in the middle: it performs quite well at our longest 5-year horizon while it struggles at the 1-year horizon. Finally, when the set of test assets is augmented with Treasury Bonds, the only model that is able to satisfy the predictors-based bounds is the rare disasters model.
Winner of the prestigious Paul A. Samuelson Award for scholarly writing on lifelong financial security, John Cochrane's Asset Pricing now appears in a revised edition that unifies and brings the science of asset pricing up to date for advanced students and professionals. Cochrane traces the pricing of all assets back to a single idea--price equals expected discounted payoff--that captures the macro-economic risks underlying each security's value. By using a single, stochastic discount factor rather than a separate set of tricks for each asset class, Cochrane builds a unified account of modern asset pricing. He presents applications to stocks, bonds, and options. Each model--consumption based, CAPM, multifactor, term structure, and option pricing--is derived as a different specification of the discounted factor. The discount factor framework also leads to a state-space geometry for mean-variance frontiers and asset pricing models. It puts payoffs in different states of nature on the axes rather than mean and variance of return, leading to a new and conveniently linear geometrical representation of asset pricing ideas. Cochrane approaches empirical work with the Generalized Method of Moments, which studies sample average prices and discounted payoffs to determine whether price does equal expected discounted payoff. He translates between the discount factor, GMM, and state-space language and the beta, mean-variance, and regression language common in empirical work and earlier theory. The book also includes a review of recent empirical work on return predictability, value and other puzzles in the cross section, and equity premium puzzles and their resolution. Written to be a summary for academics and professionals as well as a textbook, this book condenses and advances recent scholarship in financial economics.
The consumption-based models have a lack of predictive power for explaining variability of stock returns. This paper examines two well-known models, Campbell and Cochrane (1999)'s habit model and Bansal and Yaron (2004)'s long-run risks model, to see whether they produce a significant power of return predictability. For the habit model, empirical tests reveal that the state variable, the surplus consumption ratio, explains counter-cyclical time-varying expected returns. The long-run risks model also proves to explain that main sources of volatility in price-dividend ratio are a persistent and predictable consumption growth rate and fluctuating economic uncertainty. The models are also tested by following the work of Kirby (1998) whether they can explain the observed return predictability. Both models fail to generate any significant predictive power. The habit model is relatively strong in volatility, which implies that variation in expected excess return is largely attributable to the time-varying risk aversion.
New insights about the connections between stock market volatility and returns, the pricing of long-run claims, or return predictability have recently revived interest in consumption-based equilibrium asset pricing. The recursive utility model is prominently used in these contexts to determine the price of assets in equilibrium. Often, solutions are approximate and quantities of interest are computed through simulations. We propose an approach that delivers closed-form formulas for price-consumption and price-dividend ratios, as well as for many of the statistics usually computed to assess the ability of the model to reproduce stylized facts. The proposed framework is flexible enough to capture rich dynamics for consumption and dividends. Closed-form formulas facilitate the economic interpretation of empirical results. We illustrate the usefulness of our approach by investigating the properties of long-run asset pricing models in many empirical dimensions.
