845 resultados para Interval Variable
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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.
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We provide a theoretical framework to explain the empirical finding that the estimated betas are sensitive to the sampling interval even when using continuously compounded returns. We suppose that stock prices have both permanent and transitory components. The permanent component is a standard geometric Brownian motion while the transitory component is a stationary Ornstein-Uhlenbeck process. The discrete time representation of the beta depends on the sampling interval and two components labelled \"permanent and transitory betas\". We show that if no transitory component is present in stock prices, then no sampling interval effect occurs. However, the presence of a transitory component implies that the beta is an increasing (decreasing) function of the sampling interval for more (less) risky assets. In our framework, assets are labelled risky if their \"permanent beta\" is greater than their \"transitory beta\" and vice versa for less risky assets. Simulations show that our theoretical results provide good approximations for the means and standard deviations of estimated betas in small samples. Our results can be perceived as indirect evidence for the presence of a transitory component in stock prices, as proposed by Fama and French (1988) and Poterba and Summers (1988).
Harsanyi’s Social Aggregation Theorem : A Multi-Profile Approach with Variable-Population Extensions
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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.
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It is well known that standard asymptotic theory is not valid or is extremely unreliable in models with identification problems or weak instruments [Dufour (1997, Econometrica), Staiger and Stock (1997, Econometrica), Wang and Zivot (1998, Econometrica), Stock and Wright (2000, Econometrica), Dufour and Jasiak (2001, International Economic Review)]. One possible way out consists here in using a variant of the Anderson-Rubin (1949, Ann. Math. Stat.) procedure. The latter, however, allows one to build exact tests and confidence sets only for the full vector of the coefficients of the endogenous explanatory variables in a structural equation, which in general does not allow for individual coefficients. This problem may in principle be overcome by using projection techniques [Dufour (1997, Econometrica), Dufour and Jasiak (2001, International Economic Review)]. AR-types are emphasized because they are robust to both weak instruments and instrument exclusion. However, these techniques can be implemented only by using costly numerical techniques. In this paper, we provide a complete analytic solution to the problem of building projection-based confidence sets from Anderson-Rubin-type confidence sets. The latter involves the geometric properties of “quadrics” and can be viewed as an extension of usual confidence intervals and ellipsoids. Only least squares techniques are required for building the confidence intervals. We also study by simulation how “conservative” projection-based confidence sets are. Finally, we illustrate the methods proposed by applying them to three different examples: the relationship between trade and growth in a cross-section of countries, returns to education, and a study of production functions in the U.S. economy.
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We discuss statistical inference problems associated with identification and testability in econometrics, and we emphasize the common nature of the two issues. After reviewing the relevant statistical notions, we consider in turn inference in nonparametric models and recent developments on weakly identified models (or weak instruments). We point out that many hypotheses, for which test procedures are commonly proposed, are not testable at all, while some frequently used econometric methods are fundamentally inappropriate for the models considered. Such situations lead to ill-defined statistical problems and are often associated with a misguided use of asymptotic distributional results. Concerning nonparametric hypotheses, we discuss three basic problems for which such difficulties occur: (1) testing a mean (or a moment) under (too) weak distributional assumptions; (2) inference under heteroskedasticity of unknown form; (3) inference in dynamic models with an unlimited number of parameters. Concerning weakly identified models, we stress that valid inference should be based on proper pivotal functions —a condition not satisfied by standard Wald-type methods based on standard errors — and we discuss recent developments in this field, mainly from the viewpoint of building valid tests and confidence sets. The techniques discussed include alternative proposed statistics, bounds, projection, split-sampling, conditioning, Monte Carlo tests. The possibility of deriving a finite-sample distributional theory, robustness to the presence of weak instruments, and robustness to the specification of a model for endogenous explanatory variables are stressed as important criteria assessing alternative procedures.
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Affiliation: Unité de recherche en Arthrose, Centre de recherche du Centre Hospitalier de l'Université de Montréal, Hôpital Notre-Dame
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En 1992, la Cour suprême du Canada souligne l'importance de faire la distinction entre l'obligation principale de conseil qui est l'objet principal du contrat et l'obligation d'information qui est accessoire. L'obligation principale de conseil est reconnue à l'égard des professionnels des professions dites libérales à l'époque, comme les avocats et notaires dans un contrat de mandat. Par contre, l'évolution jurisprudentielle récente démontre que l'obligation de conseil ne se limite plus à l'objet principal du contrat, et que la notion de «professionnel» n'a plus le sens restrictif d'autrefois. Au Québec, l'obligation principale de conseil est reconnue dans un premier temps à l'égard des professionnels soumis au Code des professions, notamment avocats et notaires. On reconnaît aussi une telle obligation accessoire de conseil à l'égard d'autres personnes qui, bien qu'elles ne soient pas des «professionnels» au sens du Code des professions, exercent tout de même des activités de nature professionnelle. C'est le cas, par exemple, des courtiers en valeurs mobilières, des courtiers en assurances ou des institutions financières. D'ailleurs, une controverse semble régner dans le domaine bancaire sur l'étendue de l'obligation de conseil et d'information des institutions financières envers un client dans le cadre d'un contrat de prestation de services. Plus particulièrement, les tribunaux semblent partagés sur la reconnaissance ou non d'une obligation de conseil de la banque dans le cadre d'un emprunt. Certaines décisions sont à l'effet que seule une obligation d'information s'impose alors que d'autres préconisent le conseil. Afin d'élucider la confusion qui règne présentement en jurisprudence dans le domaine bancaire ainsi que dans le contrat de services et d'entreprise en général, il faut faire une étude jumelée de l'évolution de l'obligation de conseil accessoire et de l'obligation de conseil principale. Notre étude porte sur l'évolution jurisprudentielle qui tend vers une théorisation dans le contrat de services ou d'entreprise, de l'obligation de conseil qui devient alors une variante ou une intensité de l'obligation accessoire d'information. Par le fait même, on constate qu'une obligation de conseil principale semble également s'étendre à une catégorie plus large de professionnels. Dans ce cas, l'obligation de conseil est similaire, dans son fondement, au devoir de conseil du «professionnel» traditionnel, au sens du Code des professions. On constate alors que l'obligation principale de conseil n'est plus restreinte aux professionnels au sens classique du terme.
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L’étoile Wolf-Rayet WR 46 est connue pour sa variabilité complexe sur des échelles de temps relativement courtes de quelques heures et sur des échelles de temps plus longues de plusieurs mois. Des décalages périodiques mais intermittents en vitesse radiale ont déjà été observés dans ses raies d’émission optiques. Plusieurs périodes photométriques ont aussi été mesurées dans le passé. Des pulsations non-radiales, une modulation liée à la rotation rapide, ou encore la présence d’un compagnon de faible masse dont la présence reste à confirmer ont été proposées pour expliquer le comportement de l’étoile sur des échelles de temps de quelques heures. Dans un effort pour dévoiler sa vraie nature, nous avons observé WR 46 avec le satellite FUSE sur plusieurs cycles de variabilité à court terme. Nous avons trouvé des variations sur une échelle de temps d’environ 7,5 heures dans le continu ultraviolet lointain, dans l’aile bleue de la composante d’absorption du profil P Cygni du doublet de O vi 1032, 1038, ainsi que dans la composante d’absorption du profil P Cygni de S vi 933, 944. Nous avons également récupéré des données archivées de cette étoile obtenues avec le satellite XMM-Newton. La courbe de lumière en rayons X montre des variations sur une échelle de temps similaire aux courbes de lumière du continu ultraviolet et ultraviolet lointain, et le spectre rayons X de WR 46 est très mou avec un pic d’émission à des énergies plus faibles que 1 keV. Nous discutons des différentes contraintes sur la nature de la variabilité de cette étoile que ces nouvelles observations aident à poser. Parmi les scénarios suggérés, nous concluons que celui des pulsations non-radiales est le plus probable, bien que nous soyons encore loin d’une compréhension détaillée de WR 46.