Why Do Asset Prices Not Follow Random Walks?

This paper analyzes the e¤ect of non-constant elasticity of the pricing kernel on asset return characteristics in a rational expectations model. It is shown that declining elasticity of the pricing kernel can lead to predictability of asset returns and high and persistent volatility. Also, declining elasticity helps to motivate technical analysis and to explain stock market crashes. Moreover, based on a general characterization of the pricing kernel, we propose analytical asset price processes which can be tested empirically. The numerical analysis reveals strong deviations from the geometric Brownian motion which are caused by declining elasticity of the pricing kernel.Pricing Kernel, Viable asset price processes, Serial correlation, Heteroskedasticity, Stock market crashes

Pricing Kernel Specification for User Cost of Monetary Assets

This paper studies the nonlinear asset pricing kernel approximation by using orthonormal polynomials of state variables in which the pricing kernel specification is restricted by preference theory. We approximate the true asset pricing kernel for monetary assets by considering consumption-based and Fama-French asset pricing models in which the consumer is assumed to have inter-temporally non-separable preference. We study the classical consumption-based kernels and multifactor (Fama-French) kernels in our asset pricing models. Our results suggest that the multi-factor pricing kernels with nonlinearity and non-separable utility specifications have significantly improved performance

Efficient Estimation of Conditional Asset Pricing Models

A semiparametric efficient estimation procedure is developed for the parameters of multivariate GARCH-in-mean models when the disturbances have a distribution that is assumed to be elliptically symmetric but is otherwise unrestricted. Under high level restrictions, the resulting estimator achieves the asymptotic semiparametric efficiency bound. The elliptical symmetry assumption allows us to avert the curse of dimensionality problem that would otherwise arise in estimating the unknown error distribution. This framework is suitable for the estimation and testing of conditional asset pricing models such as the conditional CAPM, and we apply our estimator in an empirical study of stock prices, with Monte Carlo simulation results also being reported. Nous développons un nouvel estimateur pour les paramètres d'un modèle de GARCH en moyenne (" GARCH-M ") avec plusieurs variables. L'estimateur a l'efficacité semiparamétrique quand les erreurs suivent une loi de probabilité qui est elliptiquement symétrique mais n'aucune autre restriction. Sous les hypothèses de haut niveau, notre estimateur obtient la limite d'efficacité semiparamétrique. L'hypothèse de la symétrie elliptique nous permet d'éviter le problème d'estimer non-paramétriquement une fonction de haut dimension, parce qu'on peut écrire la densité d'un loi elliptique comme un fonction d'une transformation unidimensionnelle de la variable aléatoire multidimensionnelle. Ce cadre est approprié pour analyser des modèles conditionnels des prix des actifs financiers, comme le CAPM conditionnel. Nous appliquons notre méthodologie à l'étude des prix des actions, et nous rendons compte des résultats d'une étude simulation "Monte-Carlo".Capital asset pricing model, elliptical symmetry, semiparametric efficiency, GARCH.

Tranching and Pricing in CDO-Transactions

This paper empirically investigates the tranching and tranche pricing of European securitization transactions of corporate loans and bonds. Tranching allows the originator to issue bonds with strong quality differences and thereby attract heterogeneous investors. We find that the number of differently rated tranches in a transaction is inversely related to the quality of the underlying asset pool. Credit spreads on tranches in a transaction are inversely related to the number of tranches. The average price for transferring a unit of expected default risk, paid in a transaction, is inversely related to the default probability of the underlying asset pool. The average price, paid for a tranche, increases with the rating of the tranche, it is higher for the lowest rated tranche and very high for AAA-tranches in true sale-transactions. It varies little across butterfly spreads obtained from rated tranches except for the most senior spread.Securitization, information asymmetries, tranching of asset portfolios, risk premiums of tranches

Evaluating the Specification Errors of Asset Pricing Models

This paper examines the specification errors of several asset pricing models using the methodology of Hansen and Jagannathan (1997) and a common data set. The models are the CAPM, the Consumption CAPM, the Jagannathan and Wang (1996) conditional CAPM, the Campbell (1996) dynamic asset pricing model, the Cochrane (1996) production-based model, and the Fama-French (1993) three-factor and five-factor models. We use returns on the Fama-French twenty-five portfolios sorted by size and book-to-market ratio and the risk-free rate as our test assets. The sample is 1952 to 1997. We allow the parameters of the models' pricing kernels to fluctuate with the business cycle which we measure in two ways. One uses the Hodrick-Prescott (1997) filter applied to either industrial production for monthly models or real GNP for quarterly models. The second approach for quarterly models uses the consumption-wealth measure developed by Lettau and Ludvigson (1999). While we cannot reject correct pricing for Campbell's model, a stability test indicates that the parameters may not be stable. None of the models correctly prices returns that are scaled by the term premium.

Asset Pricing Theories, Models, and Tests

An important but still partially unanswered question in the investment field is why different assets earn substantially different returns on average. Financial economists have typically addressed this question in the context of theoretically or empirically motivated asset pricing models. Since many of the proposed “risk” theories are plausible, a common practice in the literature is to take the models to the data and perform “horse races” among competing asset pricing specifications. A “good” asset pricing model should produce small pricing (expected return) errors on a set of test assets and should deliver reasonable estimates of the underlying market and economic risk premia. This chapter provides an up-to-date review of the statistical methods that are typically used to estimate, evaluate, and compare competing asset pricing models. The analysis also highlights several pitfalls in the current econometric practice and offers suggestions for improving empirical tests

Benchmarks in Aggregate Household Portfolios

Reference-dependent preference models assume that agents derive utility from deviations of consumption from benchmark levels, rather than from consumption levels. These references can be either backward-looking (as explicit in the Habit literature) or forward-looking (as implicitly suggested by Prospect Theory). For both cases, we specify and estimate a fully structural multi-variate Brownian system in optimal consumption, portfolio and wealth using aggregate household financial and real estate wealth data. Our results reveal that references are (i) strongly relevant, (ii) state-dependent, and (iii) that the data is more consistent with the backward- than the forward-looking reference model.portfolio choice; reference-dependent utility; habit; prospect; estimation of diffusion processes