[Probably not. First, allowing the probabilities of the states of the economy to differ from their sample frequencies, the consumption-CAPM is still rejected in both U.S. and international data. Second, the recorded world disasters are too small to rationalize the puzzle, unless one assumes that disasters occur every 6-10 years. Third, if the data were generated by the rare events distribution needed to rationalize the equity premium puzzle, the puzzle itself would be unlikely to arise. Fourth, the rare events hypothesis, by reducing the cross-sectional dispersion of consumption risk, worsens the ability of the consumption-CAPM to explain the cross-section of returns.]

A reduction in in ation can fuel run-ups in housing prices if people suer from money illusion. For example, investors who decide whether to rent or buy a house by simply comparing monthly rent and mortgage payments do not take into account the fact that in ation lowers future real mortgage costs. We decompose the price-rent ratio into a rational component -meant to capture the "proxy eect" and risk premia -and an implied mispricing. We nd that in ation and nominal interest rates explain a large share of the time-series variation of the mispricing, and that the tilt eect is very unlikely to rationalize this nding.

[A reduction in inflation can fuel run-ups in housing prices if people suffer from money illusion. For example, investors who decide whether to rent or buy a house by simply comparing monthly rent and mortgage payments do not take into account the fact that inflation lowers future real mortgage costs. We decompose the price-rent ratio into a rational component--meant to capture the "proxy effect" and risk premia--and an implied mispricing. We find that inflation and nominal interest rates explain a large share of the time series variation of the mispricing, and that the tilt effect is very unlikely to rationalize this finding.]

This paper evaluates the central insight of the consumption capital asset pricing model that an asset's expected return is determined by its equilibrium risk to consumption. Rather than measure risk by the contemporaneous covariance of an asset's return and consumption growth, we measure risk by the covariance of an asset's return and consumption growth cumulated over many quarters following the return. While contemporaneous consumption risk explains little of the variation in average returns across the 25 Fama‐French portfolios, our measure of ultimate consumption risk at a horizon of three years explains a large fraction of this variation.

Probably not. First, allowing the probabilities of the states of the economy to differ from their sample frequencies, the consumption-CAPM is still rejected in both U.S. and international data. Second, the recorded world disasters are too small to rationalize the puzzle, unless one assumes that disasters occur every 6--10 years. Third, if the data were generated by the rare events distribution needed to rationalize the equity premium puzzle, the puzzle itself would be unlikely to arise. Fourth, the rare events hypothesis, by reducing the cross-sectional dispersion of consumption risk, worsens the ability of the consumption-CAPM to explain the cross-section of returns. The Author 2012. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: [email protected]., Oxford University Press.

Using a structural model, we estimate the liquidity multiplier of an interbank network and banks’ contributions to systemic risk. To provide payment services, banks hold reserves. Their equilibrium holdings can be strategic complements or substitutes. The former arises when payment velocity and multiplier are high. The latter prevails when the opportunity cost of liquidity is large, incentivising banks to borrow neighbors’ reserves instead of holding their own. Consequently, the network can amplify or dampen shocks to individual banks. Empirically, network topology explains cross-sectional heterogeneity in banks’ systemic-risk contributions while changes in the equilibrium type drive time-series variation.

We analyze 18 quadrillion models for the joint pricing of corporate bond and stock returns. Strikingly, we find that equity and nontradable factors alone suffice to explain corporate bond risk premia once their Treasury term structure risk is accounted for, rendering the extensive bond factor literature largely redundant for this purpose. While only a handful of factors, behavioral and nontradable, are likely robust sources of priced risk, the true latent stochastic discount factor is dense in the space of observable factors. Consequently, a Bayesian Model Averaging Stochastic Discount Factor explains risk premia better than all low-dimensional models, in- and out-of-sample, by optimally aggregating dozens of factors that serve as noisy proxies for common underlying risks, yielding an out-of-sample Sharpe ratio of 1.5 to 1.8. This SDF, as well as its conditional mean and volatility, are persistent, track the business cycle and times of heightened economic uncertainty, and predict future asset returns.

We consider asset pricing models in which the SDF can be factorized into an observable component and a potentially unobservable one. Using a relative entropy minimization approach, we nonparametrically estimate the SDF and its components. Empirically, we find the SDF has a business-cycle pattern and significant correlations with market crashes and the Fama-French factors. Moreover, we derive novel bounds for the SDF that are tighter and have higher information content than existing ones. We show that commonly used consumption-based SDFs correlate poorly with the estimated one, require high risk aversion to satisfy the bounds and understate market crash risk. (

Using information in returns, we identify the stochastic process of consumption. We find that aggregate consumption reacts over multiple quarters to innovations spanned by financial markets. This persistent component accounts for over a quarter of consumption variation. These shocks command a large and significant risk premium, driving a large share of stocks' and a small yet significant fraction of bonds' time‐series variation. Nevertheless, we find no support for stochastic volatility of consumption driving time‐varying risk premia. Finally, an otherwise standard recursive utility model based on our estimated process explains equity premium and risk‐free rate puzzles with low‐risk aversion.

We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high‐dimensional problems. For a (potentially misspecified) stand‐alone model, it provides reliable price of risk estimates for both tradable and nontradable factors, and detects those weakly identified. For competing factors and (possibly nonnested) models, the method automatically selects the best specification— if a dominant one exists—or provides a Bayesian model averaging–stochastic discount factor (BMA‐SDF), if there is no clear winner. We analyze 2.25 quadrillion models generated by a large set of factors and find that the BMA‐SDF outperforms existing models in‐ and out‐of‐sample.