Seasonality and Consumption-Based Asset Pricing

Most of the evidence on consumption-based asset pricing is based on seasonally adjusted consumption data. The consumption-based models have not worked well for explaining asset returns, but with seasonally adjusted data there are reasons to expect spurious rejections of the models. This paper examines asset pricing models using not seasonally adjusted aggregate consumption data. We find evidence against models with time-separable preferences, even when the models incorporate seasonality and allow seasonal heteroskedasticity. A model that uses not seasonally adjusted consumption data and nonseparable preferences with seasonal effects works better according to several criteria. The parameter estimates imply a form of seasonal habit persistence in aggregate consumption expenditures.     

Robust Inference for Consumption-Based Asset Pricing

The reliability of traditional asset pricing tests depends on: (i) the correlations between asset returns and factors; (ii) the time series sample size T compared to the number of assets N. For macro-risk factors, like consumption growth, (i) and (ii) are often such that traditional tests cannot be trusted. We extend the Gibbons-Ross-Shanken statistic to test identification of risk premia and construct their 95% confidence sets. These sets are wide or unbounded when T and N are close, but show that average returns are not fully spanned by betas when T exceeds N considerably. Our findings indicate when meaningful empirical inference is feasible.     

Dynamic Spanning in the Consumption-Based Capital Asset Pricing Model

Under the assumptions of the Consumption-based Capital AssetPricing Model (CCAPM), Pareto optimal consumption allocationsare characterized by each agent's consumption process beingadapted to the filtration generated by the aggregate consumptionprocess of the economy. The wealth processes of the agents,however, are adapted to the finer filtration generated by aggregateconsumption and the conditional distribution of future aggregateconsumption. Therefore, in order to achieve pareto optimal consumptionallocations, a sufficiently varied set of assets must existsuch that any wealth process adapted to this finer filtrationcan be implemented by dynamically trading in that set of assets.We provide sufficient conditions for the existence of such aset of assets based on dynamically trading contingent claimson aggregate consumption. In addition, we give sufficient conditionsfor the existence of equilibria in a dynamically effectivelycomplete market in which agents are only able to trade in contingentclaims on aggregate consumption, the market portfolio of firms,and a (numeraire) zero-coupon bond. We demonstrate the roleof short- and long-term contingent claims on aggregate consumptionfor the implementation of Pareto optimal allocations inthe presenceof short- and long-term risks. In addition, in the presenceof personal risks, we demonstrate the role of insurance contracts.JEL Classification: G13.     

Dynamic Spanning in the Consumption-Based Capital Asset Pricing Model

Under the assumptions of the Consumption-based Capital Asset Pricing Model (CCAPM), Pareto optimal consumption allocations are characterized by each agent's consumption process being adapted to the filtration generated by the aggregate consumption process of the economy. The wealth processes of the agents, however, are adapted to the finer filtration generated by aggregate consumption and the conditional distribution of future aggregate consumption. Therefore, in order to achieve Pareto optimal consumption allocations, a sufficiently varied set of assets must exist such that any wealth process adapted to this finer filtration can be implemented by dynamically trading in that set of assets. We provide sufficient conditions for the existence of such a set of assets based on dynamically trading contingent claims on aggregate consumption. In addition, we give sufficient conditions for the existence of equilibria in a dynamically effectively complete market in which agents are only able to trade in contingent claims on aggregate consumption, the market portfolio of firms, and a (numeraire) zero-coupon bond. We demonstrate the role of short- and long-term contingent claims on aggregate consumption for the implementation of Pareto optimal allocations in the presence of short- andlong-term risks. In addition, in the presence of personal risks, we demonstrate the role of insurance contracts.     

Comparing Asset Pricing Models

A Bayesian asset pricing test is derived that is easily computed in closed form from the standard F‐statistic. Given a set of candidate traded factors, we develop a related test procedure that permits the computation of model probabilities for the collection of all possible pricing models that are based on subsets of the given factors. We find that the recent models of Hou, Xue, and Zhang (2015a, 2015b) and Fama and French (2015, 2016) are dominated by a variety of models that include a momentum factor, along with value and profitability factors that are updated monthly.     

Multifactor Asset Pricing Models

Since the early 1960s, the mean-variance Capital Asset Pricing Model (CAPM) has been a dominant paradigm in modern finance. Recently, the accumulation of anomalous evidence, and a realisation that empirical tests of the model are tautologically related to the efficiency of the market index, have pushed that paradigm to a point of crisis. This paper reviews alternative asset pricing models which coexisted with the CAPM and may provide plausible substitutes. The major distinguishing feature of these models is that they predict multiple risk factors and, with the exception of the Arbitrage Pricing Theory (APT), are extensions of the CAPM.     

Labor Mobility: Implications for Asset Pricing

Labor mobility is the flexibility of workers to walk away from an industry in response to better opportunities. I develop a model in which labor flows make bad times worse for shareholders who are left with capital that is less productive. The model shows that firms face greater operating leverage by providing flexibility to mobile workers. I construct an empirical measure of labor mobility consistent with the model and document an economically significant cross‐sectional relation between mobility, operating leverage, and stock returns. I find that firms in mobile industries earn returns over 5% higher than those in less mobile industries.     

On Comparing Asset Pricing Models

Revisiting the framework of (Barillas, Francisco, and Jay Shanken, 2018, Comparing asset pricing models, The Journal of Finance 73, 715–754). BS henceforth, we show that the Bayesian marginal likelihood-based model comparison method in that paper is unsound : the priors on the nuisance parameters across models must satisfy a change of variable property for densities that is violated by the Jeffreys priors used in the BS method. Extensive simulation exercises confirm that the BS method performs unsatisfactorily. We derive a new class of improper priors on the nuisance parameters, starting from a single improper prior, which leads to valid marginal likelihoods and model comparisons. The performance of our marginal likelihoods is significantly better, allowing for reliable Bayesian work on which factors are risk factors in asset pricing models.     

Portfolio Construction for Tests of Asset Pricing Models

Portfolios are commonly used in finance literature to study asset‐pricing models. In business practice portfolios are used to detect abnormal performance in certain asset groups or to construct reference assets. However, analyses on practical issues related to portfolio construction are surprisingly few. This paper presents and discusses issues related to portfolio return calculation from theoretical and practical perspectives. Special attention is given both to smaller and emerging stock markets. These stock markets often share common features like low liquidity, multiple stock series, and changes in foreign ownership restrictions that greatly affect portfolio construction.     

Portfolio Selection and Asset Pricing Models

Finance theory can be used to form informative prior beliefs in financial decision making. This paper approaches portfolio selection in a Bayesian framework that incorporates a prior degree of belief in an asset pricing model. Sample evidence on home bias and value and size effects is evaluated from an asset-allocation perspective. U.S. investors' belief in the domestic CAPM must be very strong to justify the home bias observed in their equity holdings. The same strong prior belief results in large and stable optimal positions in the Fama–French book-to-market portfolio in combination with the market since the 1940s.     

An Investigation of Commodity Futures Prices Using the Consumption-Based Intertemporal Capital Asset Pricing Model

In this paper we extend the multigood futures pricing model of Grauer and Litzenberger 9 to a dynamic discrete time setting. We then test the model using data on futures prices for corn, wheat, and soybeans. The parameter estimates we obtain are similar to those obtained by other researchers using stock return data. The model itself is rejected and we offer some suggestions as to which assumption may be violated. We also give an interpretation to the Hansen-Singleton nonlinear instrumental variables estimation technique used in our empirical work.     

Asset Pricing Implications of Firms' Financing Constraints

We use a production-based asset pricing model to investigatewhether financing constraints are quantitatively important forthe cross-section of returns. Specifically, we use GMM to explorethe stochastic Euler equation imposed on returns by optimalinvestment. Our methods can identify the impact of financialfrictions on the stochastic discount factor with cyclical variationsin cost of external funds. We find that financing frictionsprovide a common factor that improves the pricing of cross-sectionalreturns. Moreover, the shadow cost of external funds exhibitsstrong procyclical variation, so that financial frictions aremore important in relatively good economic conditions. (JELE22, E44, G12)     

Diagnosing Asset Pricing Models Using the Distribution of Asset Returns

This paper develops a set of diagnostic tests which can shed light on why a particular model is failing and indicate what steps might be taken to make the model consistent with asset returns. Theoretical bounds on the moments of a stochastic discount factor are derived as a function of the moments of observed asset returns. Particular attention is paid to restrictions on moments other than the variance. These bounds can also be used to measure the information about the distribution of the discount factor contained in the moments of various asset returns. As an application of this methodology, bounds on the discount factor are estimated using size-based portfolios, and the results are used to analyze the small firm effect. Empirical results indicate, for the period 1926–1975, that moments of the returns of small firms contain information about the discount factor that is not contained in the moments of the returns of large firms and/or a proxy of the aggregate wealth portfolio. However, this difference disappears when more recent data is included.     

Networks in Production: Asset Pricing Implications

In this paper, I examine asset pricing in a multisector model with sectors connected through an input‐output network. Changes in the network are sources of systematic risk reflected in equilibrium asset prices. Two characteristics of the network matter for asset prices: network concentration and network sparsity. These two production‐based asset pricing factors are determined by the structure of the network and are computed from input‐output data. Consistent with the model predictions, I find return spreads of 4.6% and ?3.2% per year on sparsity and concentration beta‐sorted portfolios, respectively.     

A Simple Econometric Approach for Utility-Based Asset Pricing Models

Utility-based models of asset pricing may be estimated with or without assuming a distribution for security returns; both approaches are developed and compared here. The chief strength of a parametric estimator lies in its computational simplicity and statistical efficiency when the added distributional assumption is true. In contrast, the nonparametric estimator is robust to departures from any particular distribution, and it is more consistent with the spirit underlying utility-based asset pricing models since the distribution of asset returns remains unspecified even in the empirical work. The nonparametric approach turns out to be easy to implement with precision nearly indistinguishable from its parametric counterpart in this particular application. The application shows that log utility is consistent with the data over the period 1926–1981.     

Asset Pricing Models and Financial Market Anomalies

This article develops a framework that applies to single securitiesto test whether asset pricing models can explain the size, value,and momentum anomalies. Stock level beta is allowed to varywith firm-level size and book-to-market as well as with macroeconomicvariables. With constant beta, none of the models examined captureany of the market anomalies. When beta is allowed to vary, thesize and value effects are often explained, but the explanatorypower of past return remains robust. The past return effectis captured by model mispricing that varies with macroeconomicvariables.     

Using Expectations to Test Asset Pricing Models

Asset pricing models generate predictions relating assets' expected rates of return and their risk attributes. Most tests of these models have employed realized rates of return as a proxy for expected return. We use analysts' expected rates of return to examine the relation between these expectations and firm attributes. By assuming that analysts' expectations are unbiased estimates of market-wide expected rates of return, we can circumvent the use of realized rates of return and provide evidence on the predictions emanating from traditional asset pricing models. We find a positive, robust relation between expected return and market beta and a negative relation between expected return and firm size, consistent with the notion that these are risk factors. We do not find that high book-to-market firms are expected to earn higher returns than low book-to-market firms, inconsistent with the notion that book-to-market is a risk factor.     

Stock Return Predictability and Asset Pricing Models

This article develops an asset allocation framework that incorporatesprior beliefs about the extent of stock return predictabilityexplained by asset pricing models. We find that when prior beliefsallow even minor deviations from pricing model implications,the resulting asset allocations depart considerably from andsubstantially outperform allocations dictated by either theunderlying models or the sample evidence on return predictability.Under a wide range of beliefs about model pricing abilities,asset allocations based on conditional models outperform theirunconditional counterparts that exclude return predictability.     

Quantitative Asset Pricing Implications of Endogenous Solvency Constraints

We study the asset pricing implications of an economy wheresolvency constraints are endogenously determined to deter agentsfrom defaulting while allowing as much risk sharing as possible.We solve analytically for efficient allocations and for thecorresponding asset prices, portfolio holdings, and solvencyconstraints for a simple example. Then we calibrate a more generalmodel to U.S. aggregate as well as idiosyncratic income processes.We find equity premia, risk premia for long-term bonds, andSharpe ratios of magnitudes similar to the U.S. data for lowriskaversion and a lowtime-discount factor.     

Estimation and Evaluation of Conditional Asset Pricing Models

We find that several recently proposed consumption‐based models of stock returns, when evaluated using an optimal set of managed portfolios and the associated model‐implied conditional moment restrictions, fail to capture key features of risk premiums in equity markets. To arrive at these conclusions, we construct an optimal Generalized Method of Moments (GMM) estimator for models in which the stochastic discount factor (SDF) is a conditionally affine function of a set of priced risk factors, and we show that there is an optimal choice of managed portfolios to use in testing a null model against a proposed alternative generalized SDF.