Estimation and Test of a Simple Model of Intertemporal Capital Asset Pricing

A simple valuation model with time‐varying investment opportunities is developed and estimated. The model assumes that the investment opportunity set is completely described by the real interest rate and the maximum Sharpe ratio, which follow correlated Ornstein–Uhlenbeck processes. The model parameters and time series of the state variables are estimated using U.S. Treasury bond yields and expected inflation from January 1952 to December 2000, and as predicted, the estimated maximum Sharpe ratio is related to the equity premium. In cross‐sectional asset‐pricing tests, both state variables have significant risk premia, which is consistent with Merton's ICAPM.     

Investment Policy Implications of the Capital Asset Pricing Model

The results of previous generalized Security Market Line (SML) tests of the Mean Variance (MV) and Linear Risk Tolerance (LRT) Capital Asset Pricing Models indicate that the models are empirically identical. A very widely accepted, but technically incorrect, explanation for the results is that with normal return distributions all expected utility maximizing riskaverse investors will pick MV portfolios. The paper shows that the generalized SML tests cannot distinguish between the MV model and a much wider variety of power utility LRT models than has previously been entertained. On the other hand, with approximately normal, or real world, return distributions the investment policies of the various models are shown to be different from each other, and from the MV policy in particular. To the extent the results of the portfolio selection calculations are robust, the results of, and implications drawn from, the tests of the macro pricing relations are not based on firm micro foundations.     

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.     

An Intertemporal International Asset Pricing Model: Theory and Empirical Evidence

We extend Campbell's (1993) model to develop an intertemporal international asset pricing model (IAPM). We show that the expected international asset return is determined by a weighted average of market risk, market hedging risk, exchange rate risk and exchange rate hedging risk. These weights sum up to one. Our model explicitly separates hedging against changes in the investment opportunity set from hedging against exchange rate changes as well as exchange rate risk from intertemporal hedging risk. A test of the conditional version of our intertemporal IAPM using a multivariate GARCH process supports the asset pricing model. We find that the exchange rate risk is important for pricing international equity returns and it is much more important than intertemporal hedging risk.     

A Labor Capital Asset Pricing Model

We show that labor search frictions are an important determinant of the cross‐section of equity returns. Empirically, we find that firms with low loadings on labor market tightness outperform firms with high loadings by 6% annually. We propose a partial equilibrium labor market model in which heterogeneous firms make dynamic employment decisions under labor search frictions. In the model, loadings on labor market tightness proxy for priced time‐variation in the efficiency of the aggregate matching technology. Firms with low loadings are more exposed to adverse matching efficiency shocks and require higher expected stock returns.     

Temporal Aggregation and the Continuous-Time Capital Asset Pricing Model

We examine how the empirical implications of the Capital Asset Pricing Model (CAPM) are affected by the length of the period over which returns are measured. We show that the continuous-time CAPM becomes a multifactor model when the asset pricing relation is aggregated temporally. We use Hansen's Generalized Method of Moments (GMM) approach to test the continuous-time CAPM at an unconditional level using size portfolio returns. The results indicate that the continuous-time CAPM cannot be rejected. In contrast, the discrete-time CAPM is easily rejected by the tests. These results have a number of important implications for the interpretation of tests of the CAPM which have appeared in the literature.     

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.     

The Market Model and Capital Asset Pricing Theory: A Note

This note shows that a linear market model is sufficient to derive a linear relationship between beta and expected return. Furthermore, the slope of the relationship will be identical with that of the Capital Asset Pricing Model if the return on the market portfolio is normally distributed. However, results from characterization theory suggest that the linear market model assumption is close to that of multivariate normality.     

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.     

Co-Kurtosis and Capital Asset Pricing

This paper examines the impact of co-kurtosis on asset pricing using a four-moment capital asset pricing model. It is shown that, in the presence of skewness and kurtosis in asset return distribution, the expected excess rate of return is related not only to the systematic variance but also to the systematic skewness and systematic kurtosis. Investors are compensated in higher expected return for bearing the systematic variance and the systematic kurtosis risks. Investors also forego the expected excess return for taking the benefit of increasing the systematic skewness.     

Application of the Capital Asset Pricing Model to Multi-period Investments

This paper presents a method for evaluating a multi-period asset that is consistent with the valuations implied by the capital asset pricing model, and shows how the results of the valuation processes can be expressed in terms of a number of commonly used valuation formulas.
Ce papier expose tout d'abord une méthode permettant d'estimer une valeur immobilisée à multiples périodes qui soit compatible avec les évaluations fournies par le modéle d'bvaluation de l'actif immobilisé. II montre ensuite comment les résultats des opérations d'haluation peuvent être exprimés au moyen d'un nombre de formules d'estimation couramment utilisées.
Dieser Beitrag stellt eine Methode für die Bewertung einer mehrperiodischen Investition dar, die mit den Bewertungen, die das 'Capital Asset Pricing' Modell impliziert, übereinstimmt. Er zeigt, wie die Ergebnisse des Bewertungsprozesses mittels einer Anzahl gebrluchlicher Formeln ausgedruckt werden konnen.     

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.     

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.