Latent Variable Models for Stochastic Discount Factors.

Latent variable models in finance originate both from asset pricing theory and time series analysis. These two strands of literature appeal to two different concepts of latent structures, which are both useful to reduce the dimension of a statistical model specified for a multivariate time series of asset prices. In the CAPM or APT beta pricing models, the dimension reduction is cross-sectional in nature, while in time-series state-space models, dimension is reduced longitudinally by assuming conditional independence between consecutive returns, given a small number of state variables. In this paper, we use the concept of Stochastic Discount Factor (SDF) or pricing kernel as a unifying principle to integrate these two concepts of latent variables. Beta pricing relations amount to characterize the factors as a basis of a vectorial space for the SDF. The coefficients of the SDF with respect to the factors are specified as deterministic functions of some state variables which summarize their dynamics. In beta pricing models, it is often said that only the factorial risk is compensated since the remaining idiosyncratic risk is diversifiable. Implicitly, this argument can be interpreted as a conditional cross-sectional factor structure, that is, a conditional independence between contemporaneous returns of a large number of assets, given a small number of factors, like in standard Factor Analysis. We provide this unifying analysis in the context of conditional equilibrium beta pricing as well as asset pricing with stochastic volatility, stochastic interest rates and other state variables. We address the general issue of econometric specifications of dynamic asset pricing models, which cover the modern literature on conditionally heteroskedastic factor models as well as equilibrium-based asset pricing models with an intertemporal specification of preferences and market fundamentals. We interpret various instantaneous causality relationships between state variables and market fundamentals as leverage effects and discuss their central role relative to the validity of standard CAPM-like stock pricing and preference-free option pricing.

Jumps in the Volatility of Financial Markets.

Recent work suggests that the conditional variance of financial returns may exhibit sudden jumps. This paper extends a non-parametric procedure to detect discontinuities in otherwise continuous functions of a random variable developed by Delgado and Hidalgo (1996) to higher conditional moments, in particular the conditional variance. Simulation results show that the procedure provides reasonable estimates of the number and location of jumps. This procedure detects several jumps in the conditional variance of daily returns on the S&P 500 index.

Testing Normality : A GMM Approach

In this paper, we consider testing marginal normal distributional assumptions. More precisely, we propose tests based on moment conditions implied by normality. These moment conditions are known as the Stein (1972) equations. They coincide with the first class of moment conditions derived by Hansen and Scheinkman (1995) when the random variable of interest is a scalar diffusion. Among other examples, Stein equation implies that the mean of Hermite polynomials is zero. The GMM approach we adopted is well suited for two reasons. It allows us to study in detail the parameter uncertainty problem, i.e., when the tests depend on unknown parameters that have to be estimated. In particular, we characterize the moment conditions that are robust against parameter uncertainty and show that Hermite polynomials are special examples. This is the main contribution of the paper. The second reason for using GMM is that our tests are also valid for time series. In this case, we adopt a Heteroskedastic-Autocorrelation-Consistent approach to estimate the weighting matrix when the dependence of the data is unspecified. We also make a theoretical comparison of our tests with Jarque and Bera (1980) and OPG regression tests of Davidson and MacKinnon (1993). Finite sample properties of our tests are derived through a comprehensive Monte Carlo study. Finally, three applications to GARCH and realized volatility models are presented.

Harsanyi’s Social Aggregation Theorem : A Multi-Profile Approach with Variable-Population Extensions

This paper provides new versions of Harsanyi’s social aggregation theorem that are formulated in terms of prospects rather than lotteries. Strengthening an earlier result, fixed-population ex-ante utilitarianism is characterized in a multi-profile setting with fixed probabilities. In addition, we extend the social aggregation theorem to social-evaluation problems under uncertainty with a variable population and generalize our approach to uncertain alternatives, which consist of compound vectors of probability distributions and prospects.

Methods to Estimate Dynamic Stochastic General Equilibrium Models

This paper employs the one-sector Real Business Cycle model as a testing ground for four different procedures to estimate Dynamic Stochastic General Equilibrium (DSGE) models. The procedures are: 1 ) Maximum Likelihood, with and without measurement errors and incorporating Bayesian priors, 2) Generalized Method of Moments, 3) Simulated Method of Moments, and 4) Indirect Inference. Monte Carlo analysis indicates that all procedures deliver reasonably good estimates under the null hypothesis. However, there are substantial differences in statistical and computational efficiency in the small samples currently available to estimate DSGE models. GMM and SMM appear to be more robust to misspecification than the alternative procedures. The implications of the stochastic singularity of DSGE models for each estimation method are fully discussed.

The shunt from the cyclooxygenase to lipoxygenase pathway in human osteoarthritic subchondral osteoblasts is linked with a variable expression of the 5-lipoxygenase-activating protein

Affiliation: Unité de recherche en Arthrose, Centre de recherche du Centre Hospitalier de l'Université de Montréal, Hôpital Notre-Dame

From Smeed's Single-Damage Fixed-Form Three-Variable Model to Multinational and Multiprovincial Drag-Type Formulations: Does the Mystery of 1972-1973 Disapear ?

Rapport de recherche

Les erreurs de mesure sur la variable indépendante, dans le cadre de séries chronologiques.

Rapport de recherche

An Autoregressive Spectral Density Estimator at Frequency Zero for Nonstationarity Tests.

Many unit root and cointegration tests require an estimate of the spectral density function at frequency zero at some process. Kernel estimators based on weighted sums of autocovariances constructed using estimated residuals from an AR(1) regression are commonly used. However, it is known that with substantially correlated errors, the OLS estimate of the AR(1) parameter is severely biased. in this paper, we first show that this least squares bias induces a significant increase in the bias and mean-squared error of kernel-based estimators.

Développement d’une méthode d’estimation du contenu en glutamine des aliments

Cette étude vise à estimer l’apport en glutamine (Gln) alimentaire chez des athlètes soumis à un protocole de supplémentation en glutamine ainsi qu’à clarifier les informations diffusées au grand public en ce qui concerne les sources alimentaires de glutamine. Des études cliniques ont démontré que la supplémentation en glutamine pouvait réduire la morbidité et la mortalité chez des sujets en phase critique (grands brulés, chirurgie…). Le mécanisme en cause semble impliquer le système immunitaire. Cependant, les études chez les sportifs, dont le système immunitaire a de fortes chances d’être affaibli lors de périodes d’entraînement prolongées impliquant des efforts longs et intenses, n’ont pas été concluantes. Or, ces études négligent systématiquement l’apport alimentaire en glutamine, si bien qu’il est probable que les résultats contradictoires observés puissent en partie être expliqués par les choix alimentaires des sujets. Puisque la méthode conventionnelle de dosage des acides aminés dans les protéines alimentaires transforme la glutamine en glutamate, les tables de composition des aliments présentent la glutamine et le glutamate ensemble sous la dénomination « glutamate » ou « Glu », ce qui a comme conséquence de créer de l’ambiguïté. La dénomination « Glx » devrait être utilisée. Partant de la probabilité qu’un apport en Glx élevé soit un bon indicateur de l’apport en glutamine, nous avons créé un calculateur de Glx et avons évalué l’alimentation de 12 athlètes faisant partie d’une étude de supplémentation en glutamine. Nous avons alors constaté que l’apport en Glx était directement proportionnel à l’apport en protéines, avec 20,64 % ± 1,13 % de l’apport protéique sous forme de Glx. Grâce à quelques données sur la séquence primaire des acides aminés, nous avons pu constater que le rapport Gln/Glx pouvait être très variable d’un type de protéine à l’autre. Alors que le ratio molaire Gln/Glx est de ~95 % pour les α et β-gliadines, il n’est que de ~43 % pour la caséine, de ~36 % pour la β-lactoglobuline, de ~31 % pour l’ovalbumine et de ~28 % pour l’actine. Il est donc possible que certaines protéines puissent présenter des avantages par rapport à d’autres, à quantité égale de Glx.