995 resultados para Stochastic Integral


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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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The GARCH and Stochastic Volatility paradigms are often brought into conflict as two competitive views of the appropriate conditional variance concept : conditional variance given past values of the same series or conditional variance given a larger past information (including possibly unobservable state variables). The main thesis of this paper is that, since in general the econometrician has no idea about something like a structural level of disaggregation, a well-written volatility model should be specified in such a way that one is always allowed to reduce the information set without invalidating the model. To this respect, the debate between observable past information (in the GARCH spirit) versus unobservable conditioning information (in the state-space spirit) is irrelevant. In this paper, we stress a square-root autoregressive stochastic volatility (SR-SARV) model which remains true to the GARCH paradigm of ARMA dynamics for squared innovations but weakens the GARCH structure in order to obtain required robustness properties with respect to various kinds of aggregation. It is shown that the lack of robustness of the usual GARCH setting is due to two very restrictive assumptions : perfect linear correlation between squared innovations and conditional variance on the one hand and linear relationship between the conditional variance of the future conditional variance and the squared conditional variance on the other hand. By relaxing these assumptions, thanks to a state-space setting, we obtain aggregation results without renouncing to the conditional variance concept (and related leverage effects), as it is the case for the recently suggested weak GARCH model which gets aggregation results by replacing conditional expectations by linear projections on symmetric past innovations. Moreover, unlike the weak GARCH literature, we are able to define multivariate models, including higher order dynamics and risk premiums (in the spirit of GARCH (p,p) and GARCH in mean) and to derive conditional moment restrictions well suited for statistical inference. Finally, we are able to characterize the exact relationships between our SR-SARV models (including higher order dynamics, leverage effect and in-mean effect), usual GARCH models and continuous time stochastic volatility models, so that previous results about aggregation of weak GARCH and continuous time GARCH modeling can be recovered in our framework.

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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.

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The paper investigates the pricing of derivative securities with calendar-time maturities.

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This paper prepared for the Handbook of Statistics (Vol.14: Statistical Methods in Finance), surveys the subject of stochastic volatility. the following subjects are covered: volatility in financial markets (instantaneous volatility of asset returns, implied volatilities in option prices and related stylized facts), statistical modelling in discrete and continuous time and, finally, statistical inference (methods of moments, quasi-maximum likelihood, likelihood-based and bayesian methods and indirect inference).

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Tesis (Doctor en Ingeniería Industrial) U.A.N.L.

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Nous avons développé un modèle qui cherche à identifier les déterminants des trajectoires scolaires des élèves universitaires en articulant deux perspectives théoriques et en utilisant une approche méthodologique mixte en deux phases : quantitative et qualitative. La première phase est basée sur le modèle de Tinto (1992) avec l'incorporation d'autres variables de Crespo et Houle (1995). Cette étape a atteint deux objectifs. Dans le premier, on a identifié les différences entre les variables exogènes (indice économique, l'éducation parentale, moyen au lycée et moyenne dans l’examen d'entrée) et trois types de trajectoires: la persévérante, de décalage et d’abandon. Cette phase était basée sur les données d'un sondage administré à 800 étudiants à l'Université de Sonora (Mexique). Les résultats montrent que ceux qui ont quitté l'institution ont obtenu des scores significativement plus bas sur les variables exogènes. Le deuxième objectif a été atteint pour les trajectoires persévérantes et de décalage, en établissant que les étudiants ont une plus grande chance d’être persévérants lorsqu’ils présentent de meilleurs scores dans deux variables exogènes (l'examen d'entrée et être de genre féminin) et quatre viable endogènes (haute intégration académique, de meilleures perspectives d'emploi, ont une bourse). Dans la deuxième phase nous avons approfondi la compréhension (Verstehen) des processus d'articulation entre l'intégration scolaire et sociale à travers de trois registres proposés par Dubet (2005): l'intégration, le projet et la vocation. Cette phase a consisté dans 30 interviews avec étudiantes appartenant aux trois types de trajectoire. À partir du travail de Bourdages (1994) et Guzman (2004), nous avons cherché le sens de l'expérience attribuée par les étudiants au processus éducatif. Les résultats révèlent cinq groupes d’étudiantes avec des expériences universitaires identifiables : ceux qui ont une intégration académique et sociale plus grande, les femmes travailleuses intégrées académiquement, ceux qui ont les plus grandes désavantages économiques et d’intégration scolaire, ceux qui ont cherché leur vocation dans un autre établissement et ceux qui n'ont pas poursuivi leurs études. L'utilisation de différents outils statistiques (analyse de corrélation, analyse de régression logistique et analyse des conglomérats) dans la première phase a permis d’identifier des variables clés dans chaque type de trajectoire, lesquelles ont été validées avec les résultats de la phase qualitative. Cette thèse, en plus de montrer l'utilité d'une approche méthodologique mixte, étend le modèle de Tinto (1987) et confirme l'importance de l'intégration scolaire pour la persévérance à l'université.

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

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Thèse diffusée initialement dans le cadre d'un projet pilote des Presses de l'Université de Montréal/Centre d'édition numérique UdeM (1997-2008) avec l'autorisation de l'auteur.

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Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal