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Uma correta especificação das funções densidade de probabilidade (fdp’s) de retornos de ativos é um tópico dos mais relevantes na literatura de modelagem econométrica de dados financeiros. A presente dissertação propõe-se a oferecer, neste âmbito, uma abordagem distinta, através de uma aplicação da metodologia desenvolvida em Bathia et al. (2010) a dados intradiários do índice bovespa. Esta abordagem consiste em focar a análise diretamente sobre a estrutura dinâmica das fdp’s dos retornos, enxergando-as como uma sequência de variáveis aleatórias que tomam valores em um espaço de funções. A dependência serial existente entre essas curvas permite que se obtenham estimativas filtradas das fdp’s, e mesmo que se façam previsões sobre densidades de períodos subsequentes à amostra. No artigo que integra esta dissertação, onde é feita a mencionada aplicação, encontrou-se evidência de que o comportamento dinâmico das fdp’s dos retornos do índice bovespa se reduz a um processo bidimensional, o qual é bem representado por um modelo var(1) e cuja dinâmica afeta a dispersão e a assimetria das distribuições no suceder dos dias. Ademais, utilizando-se de subamostras, construíram-se previsões um passo à frente para essas fdp’s, e avaliaram-se essas previsões de acordo com métricas apropriadas. / Adequate specification of the probability density functions (pdf’s) of asset returns is a most relevant topic in econometric modelling of financial data. This dissertation aims to provide a distinct approach on that matter, through applying the methodology developed in Bathia et al. (2010) to intraday bovespa index data. This approach consists in focusing the analysis directly on the dynamic structure of returns fdp’s, seeing them as a sequence of function-valued random variables. The serial dependence of these curves allows one to obtain filtered estimates of the pdf’s, and even to forecast upcoming densities. In the paper contained into this dissertation, evidence is found that the dynamic structure of the bovespa index returns pdf’s reduces to a R2-valued process, which is well represented by a var(1) model, and whose dynamics affect the dispersion and symmetry of the distributions at each day. Moreover, one-step-ahead forecasts of upcoming pdf’s were constructed through subsamples and evaluated according to appropriate metrics.

Uma correta especificação das funções densidade de probabilidade (fdp’s) de retornos de ativos é um tópico dos mais relevantes na literatura de modelagem econométrica de dados financeiros. A presente dissertação propõe-se a oferecer, neste âmbito, uma abordagem distinta, através de uma aplicação da metodologia desenvolvida em Bathia et al. (2010) a dados intradiários do índice bovespa. Esta abordagem consiste em focar a análise diretamente sobre a estrutura dinâmica das fdp’s dos retornos, enxergando-as como uma sequência de variáveis aleatórias que tomam valores em um espaço de funções. A dependência serial existente entre essas curvas permite que se obtenham estimativas filtradas das fdp’s, e mesmo que se façam previsões sobre densidades de períodos subsequentes à amostra. No artigo que integra esta dissertação, onde é feita a mencionada aplicação, encontrou-se evidência de que o comportamento dinâmico das fdp’s dos retornos do índice bovespa se reduz a um processo bidimensional, o qual é bem representado por um modelo var(1) e cuja dinâmica afeta a dispersão e a assimetria das distribuições no suceder dos dias. Ademais, utilizando-se de subamostras, construíram-se previsões um passo à frente para essas fdp’s, e avaliaram-se essas previsões de acordo com métricas apropriadas. / Adequate specification of the probability density functions (pdf’s) of asset returns is a most relevant topic in econometric modelling of financial data. This dissertation aims to provide a distinct approach on that matter, through applying the methodology developed in Bathia et al. (2010) to intraday bovespa index data. This approach consists in focusing the analysis directly on the dynamic structure of returns fdp’s, seeing them as a sequence of function-valued random variables. The serial dependence of these curves allows one to obtain filtered estimates of the pdf’s, and even to forecast upcoming densities. In the paper contained into this dissertation, evidence is found that the dynamic structure of the bovespa index returns pdf’s reduces to a R2-valued process, which is well represented by a var(1) model, and whose dynamics affect the dispersion and symmetry of the distributions at each day. Moreover, one-step-ahead forecasts of upcoming pdf’s were constructed through subsamples and evaluated according to appropriate metrics.

Uma correta especificação das funções densidade de probabilidade (fdp’s) de retornos de ativos é um tópico dos mais relevantes na literatura de modelagem econométrica de dados financeiros. A presente dissertação propõe-se a oferecer, neste âmbito, uma abordagem distinta, através de uma aplicação da metodologia desenvolvida em Bathia et al. (2010) a dados intradiários do índice bovespa. Esta abordagem consiste em focar a análise diretamente sobre a estrutura dinâmica das fdp’s dos retornos, enxergando-as como uma sequência de variáveis aleatórias que tomam valores em um espaço de funções. A dependência serial existente entre essas curvas permite que se obtenham estimativas filtradas das fdp’s, e mesmo que se façam previsões sobre densidades de períodos subsequentes à amostra. No artigo que integra esta dissertação, onde é feita a mencionada aplicação, encontrou-se evidência de que o comportamento dinâmico das fdp’s dos retornos do índice bovespa se reduz a um processo bidimensional, o qual é bem representado por um modelo var(1) e cuja dinâmica afeta a dispersão e a assimetria das distribuições no suceder dos dias. Ademais, utilizando-se de subamostras, construíram-se previsões um passo à frente para essas fdp’s, e avaliaram-se essas previsões de acordo com métricas apropriadas. / Adequate specification of the probability density functions (pdf’s) of asset returns is a most relevant topic in econometric modelling of financial data. This dissertation aims to provide a distinct approach on that matter, through applying the methodology developed in Bathia et al. (2010) to intraday bovespa index data. This approach consists in focusing the analysis directly on the dynamic structure of returns fdp’s, seeing them as a sequence of function-valued random variables. The serial dependence of these curves allows one to obtain filtered estimates of the pdf’s, and even to forecast upcoming densities. In the paper contained into this dissertation, evidence is found that the dynamic structure of the bovespa index returns pdf’s reduces to a R2-valued process, which is well represented by a var(1) model, and whose dynamics affect the dispersion and symmetry of the distributions at each day. Moreover, one-step-ahead forecasts of upcoming pdf’s were constructed through subsamples and evaluated according to appropriate metrics.

De nos jours, la plupart des analyses CEM d’équipements et systèmes électroniques sont basées sur des approches quasi-déterministes dans lesquelles les paramètres internes et externes des modèles sont supposés parfaitement connus et où les incertitudes les affectant sont prises en compte sur les réponses par le biais de marges de sécurité importantes. Or, l’inconvénient de telles approches est qu’elles sont non seulement trop conservatives, mais en outre totalement inadaptées à certaines situations, notamment lorsque l’objectif de l’étude impose de prendre en compte le caractère aléatoire de ces paramètres via des modélisations stochastiques appropriées de type variables, processus ou champs aléatoires. Cette approche probabiliste a fait l’objet ces dernières années d’un certain nombre de recherches en CEM, tant au plan national qu’au plan international. Le travail présenté dans cette thèse est une contribution à ces recherches et a un double objectif : (1) développer et mettre en œuvre une méthodologie probabiliste et ses outils numériques d’accompagnement pour l’évaluation de la fiabilité et l’analyse sensibilité des équipements et systèmes électroniques en se limitant à des modélisations stochastiques par variables aléatoires ; (2) étendre cette étude au cas des modélisations stochastiques par processus et champs aléatoires dans le cadre d’une analyse prospective basée sur la résolution de l’équation aux dérivées partielles des télégraphistes à coefficients aléatoires.L’approche probabiliste mentionnée au point (1) consiste à évaluer la probabilité de défaillance d’un équipement ou d’un système électronique vis-à-vis d’un critère de défaillance donné et à déterminer l’importance relative de chacun des paramètres aléatoires en présence. Les différentes méthodes retenues à cette fin sont des adaptations à la CEM de méthodes développées dans le domaine de la mécanique aléatoire pour les études de propagation d’incertitudes. Pour le calcul des probabilités de défaillance, deux grandes catégories de méthodes sont proposées : celles basées sur une approximation de la fonction d’état-limite relative au critère de défaillance et les méthodes de Monte-Carlo basées sur la simulation numérique des variables aléatoires du modèle et l’estimation statistique des probabilités cibles. Pour l’analyse de sensibilité, une approche locale et une approche globale sont retenues. Ces différentes méthodes sont d’abord testées sur des applications académiques afin de mettre en lumière leur intérêt dans le domaine de la CEM. Elles sont ensuite appliquées à des problèmes de lignes de transmission et d’antennes plus représentatifs de la réalité.Dans l’analyse prospective, des méthodes de résolution avancées sont proposées, basées sur des techniques spectrales requérant les développements en chaos polynomiaux et de Karhunen-Loève des processus et champs aléatoires présents dans les modèles. Ces méthodes ont fait l’objet de tests numériques encourageant, mais qui ne sont pas présentés dans le rapport de thèse, faute de temps pour leur analyse complète. / Nowadays, most EMC analyzes of electronic or electrical devices are based on deterministic approaches for which the internal and external models’ parameters are supposed to be known and the uncertainties on models’ parameters are taken into account on the outputs by defining very large security margins. But, the disadvantage of such approaches is their conservative character and their limitation when dealing with the parameters’ uncertainties using appropriate stochastic modeling (via random variables, processes or fields) is required in agreement with the goal of the study. In the recent years, this probabilistic approach has been the subject of several researches in the EMC community. The work presented here is a contribution to these researches and has a dual purpose : (1) develop a probabilistic methodology and implement the associated numerical tools for the reliability and sensitivity analyzes of the electronic devices and systems, assuming stochastic modeling via random variables; (2) extend this study to stochastic modeling using random processes and random fields through a prospective analysis based on the resolution of the telegrapher equations (partial derivative equations) with random coefficients. The first mentioned probabilistic approach consists in computing the failure probability of an electronic device or system according to a given criteria and in determining the relative importance of each considered random parameter. The methods chosen for this purpose are adaptations to the EMC framework of methods developed in the structural mechanics community for uncertainty propagation studies. The failure probabilities computation is performed using two type of methods: the ones based on an approximation of the limit state function associated to the failure criteria, and the Monte Carlo methods based on the simulation of the model’s random variables and the statistical estimation of the target failure probabilities. In the case of the sensitivity analysis, a local approach and a global approach are retained. All these methods are firstly applied to academic EMC problems in order to illustrate their interest in the EMC field. Next, they are applied to transmission lines problems and antennas problems closer to reality. In the prospective analysis, more advanced resolution methods are proposed. They are based on spectral approaches requiring the polynomial chaos expansions and the Karhunen-Loève expansions of random processes and random fields considered in the models. Although the first numerical tests of these methods have been hopeful, they are not presented here because of lack of time for a complete analysis.

Ce travail présente quelques résultats concernant des méthodes numériques déterministes et probabilistes pour des équations aux dérivées partielles à coefficients aléatoires, avec des applications à l'hydrogéologie. On s'intéresse tout d'abord à l'équation d'écoulement dans un milieu poreux en régime stationnaire avec un coefficient de perméabilité lognormal homogène, incluant le cas d'une fonction de covariance peu régulière. On établit des estimations aux sens fort et faible de l'erreur commise sur la solution en tronquant le développement de Karhunen-Loève du coefficient. Puis on établit des estimations d'erreurs éléments finis dont on déduit une extension de l'estimation d'erreur existante pour la méthode de collocation stochastique, ainsi qu'une estimation d'erreur pour une méthode de Monte-Carlo multi-niveaux. On s'intéresse enfin au couplage de l'équation d'écoulement considérée précédemment avec une équation d'advection-diffusion, dans le cas d'incertitudes importantes et d'une faible longueur de corrélation. On propose l'analyse numérique d'une méthode numérique pour calculer la vitesse moyenne à laquelle la zone contaminée par un polluant s'étend. Il s'agit d'une méthode de Monte-Carlo combinant une méthode d'élements finis pour l'équation d'écoulement et un schéma d'Euler pour l'équation différentielle stochastique associée à l'équation d'advection-diffusion, vue comme une équation de Fokker-Planck. / This work presents some results about probabilistic and deterministic numerical methods for partial differential equations with stochastic coefficients, with applications to hydrogeology. We first consider the steady flow equation in porous media with a homogeneous lognormal permeability coefficient, including the case of a low regularity covariance function. We establish error estimates, both in strong and weak senses, of the error in the solution resulting from the truncature of the Karhunen-Loève expansion of the coefficient. Then we establish finite element error estimates, from which we deduce an extension of the existing error estimate for the stochastic collocation method along with an error estimate for a multilevel Monte-Carlo method. We finally consider the coupling of the previous flow equation with an advection-diffusion equation, in the case when the uncertainty is important and the correlation length is small. We propose the numerical analysis of a numerical method, which aims at computing the mean velocity of the expansion of a pollutant. The method consists in a Monte-Carlo method, combining a finite element method for the flow equation and an Euler scheme for the stochastic differential equation associated to the advection-diffusion equation, seen as a Fokker-Planck equation.

Cette thèse est une contribution à la construction de l’Approche Locale de la rupture à l’échelle microscopique à l’aide de la modélisation d’agrégats polycristallins. Elle consiste à prendre en compte la variabilité spatiale de la microstructure du matériau. Pour ce faire, la modélisation micromécanique du matériau est réalisée par la simulation d’agrégats polycristallins par éléments finis. Les champs aléatoires de contrainte (principale maximale et de clivage) dans le matériau qui représentent la variabilité spatiale de la microstructure sont ensuite modélisés par un champ aléatoire gaussien stationnaire ergodique. Les propriétés de variabilité spatiale de ces champs sont identifiés par une méthode d’identification, e.g. méthode du périodogramme, méthode du variogramme, méthode du maximum de vraisemblance. Des réalisations synthétiques des champs de contraintes sont ensuite simulées par une méthode de simulation, e.g. méthode Karhunen-Loève discrète, méthode “Circulant Embedding”, méthode spectrale, sans nouveau calcul aux éléments finis. Enfin, le modèle d’Approche Locale de la rupture par simulation de champ de contrainte de clivage permettant d’y intégrer les réalisations simulées du champ est construit pour estimer la probabilité de rupture du matériau. / This thesis is a contribution to the construction of the Local Approach to fracture at the microscopic scale using polycrystalline aggregate modeling. It consists in taking into account the spatial variability of the microstructure of the material. To do this, the micromechanical modeling is carried out by finite element analysis of polycrystalline aggregates. The random stress fields (maximum principal et cleavage stress) in the material representing the spatial variability of the microstructure are then modeled by a stationary ergodic Gaussian random field. The properties of the spatial variability of these fields are identified by an identification method, e.g. periodogram method, variogram method, maximum likelihood method. The synthetic realizations of the stress fields are then simulated by a simulation method, e.g. discrete Karhunen-Loève method, circulant embedding method, spectral method, without additional finite element calculations. Finally, a Local Approach to fracture by simulation of the cleavage stress field using the simulated realizations is constructed to estimate the rupture probability of the material.