Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-fit in Multivariate Regressions with Application to Asset Pricing Models

We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross-equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared to simulation-based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal, Student t; normal mixtures and stable error models. In the Gaussian case, finite-sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi-stage Monte Carlo test methods. For non-Gaussian distribution families involving nuisance parameters, confidence sets are derived for the the nuisance parameters and the error distribution. The procedures considered are evaluated in a small simulation experi-ment. Finally, the tests are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995.

Skewness risk and bond prices

Statistical evidence is reported that even outside disaster periods, agents face negative consumption skewness, as well as positive inflation skewness. Quantitative implications of skewness risk for nominal loan contracts in a pure exchange economy are derived. Key modeling assumptions are Epstein-Zin preferences for traders and asymmetric distributions for consumption and inflation innovations. The model is solved using a third-order perturbation and estimated by the simulated method of moments. Results show that skewness risk accounts for 6 to 7 percent of the risk premia depending on the bond maturity.

Quadratic M-Estimators for ARCH-Type Processes

This paper addresses the issue of estimating semiparametric time series models specified by their conditional mean and conditional variance. We stress the importance of using joint restrictions on the mean and variance. This leads us to take into account the covariance between the mean and the variance and the variance of the variance, that is, the skewness and kurtosis. We establish the direct links between the usual parametric estimation methods, namely, the QMLE, the GMM and the M-estimation. The ususal univariate QMLE is, under non-normality, less efficient than the optimal GMM estimator. However, the bivariate QMLE based on the dependent variable and its square is as efficient as the optimal GMM one. A Monte Carlo analysis confirms the relevance of our approach, in particular, the importance of skewness.

Exact Multivariate Tests of Asset Pricing Models with Stable Asymmetric Distributions

In this paper, we propose exact inference procedures for asset pricing models that can be formulated in the framework of a multivariate linear regression (CAPM), allowing for stable error distributions. The normality assumption on the distribution of stock returns is usually rejected in empirical studies, due to excess kurtosis and asymmetry. To model such data, we propose a comprehensive statistical approach which allows for alternative - possibly asymmetric - heavy tailed distributions without the use of large-sample approximations. The methods suggested are based on Monte Carlo test techniques. Goodness-of-fit tests are formally incorporated to ensure that the error distributions considered are empirically sustainable, from which exact confidence sets for the unknown tail area and asymmetry parameters of the stable error distribution are derived. Tests for the efficiency of the market portfolio (zero intercepts) which explicitly allow for the presence of (unknown) nuisance parameter in the stable error distribution are derived. The methods proposed are applied to monthly returns on 12 portfolios of the New York Stock Exchange over the period 1926-1995 (5 year subperiods). We find that stable possibly skewed distributions provide statistically significant improvement in goodness-of-fit and lead to fewer rejections of the efficiency hypothesis.

Estimating Nonlinear DSGE Models by the Simulated Method of Moments

This paper studies the application of the simulated method of moments (SMM) for the estimation of nonlinear dynamic stochastic general equilibrium (DSGE) models. Monte Carlo analysis is employed to examine the small-sample properties of SMM in specifications with different curvature. Results show that SMM is computationally efficient and delivers accurate estimates, even when the simulated series are relatively short. However, asymptotic standard errors tend to overstate the actual variability of the estimates and, consequently, statistical inference is conservative. A simple strategy to incorporate priors in a method of moments context is proposed. An empirical application to the macroeconomic effects of rare events indicates that negatively skewed productivity shocks induce agents to accumulate additional capital and can endogenously generate asymmetric business cycles.

Affine and generalized affine models : Theory and applications

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Trois essais sur la liquidité : ses effets sur les primes de risque, les anticipations et l'asymétrie des risques financiers

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Étude de la variabilité spectroscopique d’un échantillon d’étoiles Wolf-Rayet de type WC9

Nous savons que la grande majorité des étoiles WC9 produit de la poussière à base de carbone. Cette dernière doit se former dans des zones de très haute densité afin de survivre à l’environnement hostile qu’est celui du vent d’une étoile WR. Les étoiles WC appartenant à un système binaire WR + O produisent de la poussière quand les vents des deux étoiles entrent en collision et forment une zone de choc pouvant augmenter la densité du gaz d’un facteur 1000. Par contre, plusieurs étoiles WC9 n’ont, à ce jour, montré aucun signe de la présence d’un compagnon. Le but du projet est de tenter d’identifier un mécanisme alternatif responsable de la formation de poussière dans les étoiles WC9 n’appartenant pas à un système binaire. Nous présentons les résultats d’une campagne d’observation visant à caractériser la variabilité spectroscopique d’un échantillon de huit étoiles WC9 et une étoile WC8d. Nos résultats indiquent que la majorité des étoiles montrent des variations à grande échelle dans la raie d’émission C III 5696, soit à un niveau d’au moins 5% du flux de la raie et que les structures dans le vent ont une dispersion de vitesses de l’ordre de 150-300 km/s. De manière générale, les variations de vitesse radiales sont anti-corrélées avec le coefficient d’asymétrie de la raie, ce qui semble infirmer la présence d’un compagnon. Des observations en photométrie de l’étoile WR103 montrent une période de 9.1 ± 0.6 jours qui s’accorde avec les variations spectroscopiques et qui ne semble pas, de manière évidente, d’origine binaire.