Reatores tubulares de polimerização podem apresentar um perfil de velocidade bastante distorcido. Partindo desta observação, um modelo estocástico baseado no modelo de dispersão axial foi proposto para a representação matemática da fluidodinâmica de um reator tubular para produção de poliestireno. A equação diferencial foi obtida inserindo a aleatoriedade no parâmetro de dispersão, resultando na adição de um termo estocástico ao modelo capaz de simular as oscilações observadas experimentalmente. A equação diferencial estocástica foi discretizada e resolvida pelo método Euler-Maruyama de forma satisfatória. Uma função estimadora foi desenvolvida para a obtenção do parâmetro do termo estocástico e o parâmetro do termo determinístico foi calculado pelo método dos mínimos quadrados. Uma análise de convergência foi conduzida para determinar o número de elementos da discretização e o modelo foi validado através da comparação de trajetórias e de intervalos de confiança computacionais com dados experimentais. O resultado obtido foi satisfatório, o que auxilia na compreensão do comportamento fluidodinâmico complexo do reator estudado. / The velocity profile of polymerization tubular reactors may be very distorted. Based on this observation, a stochastic model based on the axial dispersion model was proposed for the mathematical representation of the fluid dynamics of a tubular reactor for polystyrene production. The differential equation was built by inserting randomness in the dipersion coefficient, which added a stochastic term to the model. This term was capable of simulating the experimentally observed fluctuations. The stochastic differential equation was discretized and solved by the Euler-Maruyama method adequately. An estimator function has been developed to calculate the parameter of the stochastic term, while the parameter of the deterministic term was estimated by a least squares method. A convergence analysis was carried out in order to determine the number of elements needed for the time discretization. The model was validated through comparisons of sample paths and computational confidence intervals with experimental data. The result was considered satisfactory, allowing a better understanding of the complex fluid dynamic behaviour of the analised reactor. Key-words: modelling, simulation, stochastic differential equation, polymerization tubular reactor, time residence distribution.

Cette thèse traite les jeux différentiels stochastiques de somme non nulle (JDSNN) dans le cadre de Markovien et de leurs liens avec les équations différentielles stochastiques rétrogrades (EDSR) multidimensionnelles. Nous étudions trois problèmes différents. Tout d'abord, nous considérons un JDSNN où le coefficient de dérive n'est pas borné, mais supposé uniquement à croissance linéaire. Ensuite certains cas particuliers de coefficients de diffusion non bornés sont aussi considérés. Nous montrons que le jeu admet un point d'équilibre de Nash via la preuve de l'existence de la solution de l'EDSR associée et lorsque la condition d'Isaacs généralisée est satisfaite. La nouveauté est que le générateur de l'EDSR, qui est multidimensionnelle, est de croissance linéaire stochastique par rapport au processus de volatilité. Le deuxième problème est aussi relatif au JDSNN mais les payoffs ont des fonctions d'utilité exponentielles. Les EDSRs associées à ce jeu sont de type multidimensionnelles et quadratiques en la volatilité. Nous montrons de nouveau l'existence d’un équilibre de Nash. Le dernier problème que nous traitons, est un jeu bang-bang qui conduit à des hamiltoniens discontinus. Dans ce cas, nous reformulons le théorème de vérification et nous montrons l’existence d’un équilibre de Nash qui est du type bang-bang, i.e., prenant ses valeurs sur le bord du domaine en fonction du signe de la dérivée de la fonction valeur ou du processus de volatilité. L'EDSR dans ce cas est un système multidimensionnel couplé, dont le générateur est discontinu par rapport au processus de volatilité. / This dissertation studies the multiple players nonzero-sum stochastic differential games (NZSDG) in the Markovian framework and their connections with multiple dimensional backward stochastic differential equations (BSDEs). There are three problems that we are focused on. Firstly, we consider a NZSDG where the drift coefficient is not bound but is of linear growth. Some particular cases of unbounded diffusion coefficient of the diffusion process are also considered. The existence of Nash equilibrium point is proved under the generalized Isaacs condition via the existence of the solution of the associated BSDE. The novelty is that the generator of the BSDE is multiple dimensional, continuous and of stochastic linear growth with respect to the volatility process. The second problem is of risk-sensitive type, i.e. the payoffs integrate utility exponential functions, and the drift of the diffusion is unbounded. The associated BSDE is of multi-dimension whose generator is quadratic on the volatility. Once again we show the existence of Nash equilibria via the solution of the BSDE. The last problem that we treat is a bang-bang game which leads to discontinuous Hamiltonians. We reformulate the verification theorem and we show the existence of a Nash point for the game which is of bang-bang type, i.e., it takes its values in the border of the domain according to the sign of the derivatives of the value function. The BSDE in this case is a coupled multi-dimensional system, whose generator is discontinuous on the volatility process.

Cette thèse comporte plusieurs procédures d'estimation non-paramétrique de densité de probabilité.Dans chaque cas les variables d'intérêt ne sont pas observées directement, ce qui est une difficulté majeure.La première partie traite un modèle linéaire mixte où des observations répétées sont disponibles.La deuxième partie s'intéresse aux modèles d'équations différentielles stochastiques à effets aléatoires. Plusieurs trajectoires sont observées en temps continu sur un intervalle de temps commun.La troisième partie se place dans un contexte de bruit multiplicatif.Les différentes parties de cette thèse sont reliées par un contexte commun de problème inverse et par une problématique commune: l'estimation de la densité d'une variable cachée. Dans les deux premières parties la densité d'un ou plusieurs effets aléatoires est estimée. Dans la troisième partie il s'agit de reconstruire la densité de la variable d'origine à partir d'observations bruitées.Différentes méthodes d'estimation globale sont utilisées pour construire des estimateurs performants: estimateurs à noyau, estimateurs par projection ou estimateurs construits par déconvolution.La sélection de paramètres mène à des estimateurs adaptatifs et les risques quadratiques intégrés sont majorés grâce à une inégalité de concentration de Talagrand. Une étude sur simulations de chaque estimateur illustre leurs performances. Un jeu de données neuronales est étudié grâce aux procédures mises en place pour les équations différentielles stochastiques. / This thesis contains several nonparametric estimation procedures of a probability density function.In each case, the main difficulty lies in the fact that the variables of interest are not directly observed.The first part deals with a mixed linear model for which repeated observations are available.The second part focuses on stochastic differential equations with random effects. Many trajectories are observed continuously on the same time interval.The third part is in a full multiplicative noise framework.The parts of the thesis are connected by the same context of inverse problems and by a common problematic: the estimation of the density function of a hidden variable.In the first two parts the density of one or two random effects is estimated. In the third part the goal is to rebuild the density of the original variable from the noisy observations.Different global methods are used and lead to well competitive estimators: kernel estimators, projection estimators or estimators built from deconvolution.Parameter selection gives adaptive estimators and the integrated risks are bounded using a Talagrand concentration inequality.A simulation study for each proposed estimator highlights their performances.A neuronal dataset is investigated with the new procedures for stochastic differential equations developed in this work.

V předložené práci je definována stochastická diferenciální rovnice a jsou uvedeny její základní vlastnosti. Stochastické diferenciální rovnice se používají k popisu fyzikálních jevů, které jsou ovlivněny i náhodnými vlivy. Řešením stochastického modelu je náhodný proces. Cílem analýzy náhodných procesů je konstrukce vhodného modelu, který umožní porozumět mechanismům, na jejichž základech jsou generována sledovaná data. Znalost modelu také umožňuje předvídání budoucnosti a je tak možné kontrolovat a optimalizovat činnost daného systému. V práci je nejdříve definován pravděpodobnostní prostor a Wienerův proces. Na tomto základě je definována stochastická diferenciální rovnice a jsou uvedeny její základní vlastnosti. Závěrečná část práce obsahuje příklad ilustrující použití stochastických diferenciálních rovnic v praxi.

No description available.

No description available.

This thesis investigated Stochastic Runge–Kutta Lawson (SRKL) schemes and their application to the Heston model. Two distinct SRKL discretization methods were used to simulate a single asset’s dynamics under the Heston model, notably the Euler–Maruyama and Midpoint schemes. Additionally, standard Monte Carlo and variance reduction techniques were implemented. European and Asian option prices were estimated and compared with a benchmark value regarding accuracy, effectiveness, and computational complexity. Findings showed that the SRKL Euler–Maruyama schemes exhibited promise in enhancing the price for simple and path-dependent options. Consequently, integrating SRKL numerical methods into option valuation provides notable advantages by addressing challenges posed by the Heston model’s SDEs. Given the limited scope of this research topic, it is imperative to conduct further studies to understand the use of SRKL schemes within other models.

Ce travail, à la frontière de l’analyse et des probabilités, s’intéresse à l’étude de systèmes différentiels a priori mal posés. Nous cherchons, grâce à des techniques issues de la théorie des chemins rugueux et de l’étude trajectorielle des processus stochastiques, à donner un sens à de tels systèmes puis à les résoudre, tout en montrant que les notions proposées ici étendent bien les notions classiques de solutions. Cette thèse se décompose en trois chapitres. Le premier traite des systèmes différentiels ordinaires perturbés additivement par des processus irréguliers éventuellement stochastiques ainsi que des effets de régularisation de tels processus. Le deuxième chapitre concerne l’équation de transport linéaire perturbée multiplicativement par des chemins rugueux ; enfin, le dernier chapitre s’intéresse à une équation de la chaleur non linéaire perturbée par un bruit blanc espace-temps, l’équation de quantisation stochastique phi4 en dimension 3. / In this work we investigate a priori ill-posed differential systems from an analytic and probabilistic point of view. Thanks to technics inspired by the rough path theory and pathwise study of stochastic processes, we want to define those ill-posed systems and then study them. The first chapter of this thesis is related to ordinary differential equations perturbed by some irregular (stochastic) processes and the effects induced by the regularization of such processes. The second chapter deals with the linear transport equation multiplicatively perturbed by a rough path. Finally, in the last chapter we investigate the stochastic quantization equation Phi4 in three dimensions.

This thesis is devoted to the study of the applications of the error theory using Dirichlet forms. Our work is split into three parts. The first one deals with the models described by stochastic differential equations. After a short technical chapter, an innovative model for order books is proposed. We assume that the bid-ask spread is not an imperfection, but an intrinsic property of exchange markets instead. The uncertainty is carried by the Brownian motion guiding the asset. We find that spread evolutions can be evaluated using closed formulae and we estimate the impact of the underlying uncertainty on the related contingent claims. Afterwards, we deal with the PBS model, a new model to price European options. The seminal idea is to distinguish the market volatility with respect to the parameter used by traders for hedging. We assume the former constant, while the latter volatility being an erroneous subjective estimation of the former. We prove that this model anticipates a bid-ask spread and a smiled implied volatility curve. Major properties of this model are the existence of closed formulae for prices, the impact of the underlying drift and an efficient calibration strategy. The second part deals with the models described by partial differential equations. Linear and non-linear PDEs are examined separately. In the first case, we show some interesting relations between the error and wavelets theories. When non-linear PDEs are concerned, we study the sensitivity of the solution using error theory. Except when exact solution exists, two possible approaches are detailed: first, we analyze the sensitivity obtained by taking "derivatives" of the discrete governing equations. Then, we study the PDEs solved by the sensitivity of the theoretical solutions. In both cases, we show that sharp and bias solve linear PDE depending on the solution of the former PDE itself and we suggest algorithms to evaluate numerically the sensitivities. Finally, the third part is devoted to stochastic partial differential equations. Our analysis is split into two chapters. First, we study the transmission of an uncertainty, present on starting conditions, on the solution of SPDE. Then, we analyze the impact of a perturbation of the functional terms of SPDE and the coefficient of the related Green function. In both cases, we show that the sharp and bias verify linear SPDE depending on the solution of the former SPDE itself

Im Rahmen dieser Dissertation bemühe ich mich, den statistischen Ansatz der zeitkontinuierlichen dynamischen Modellierung, der die Rolle der Zeit explizit berücksichtigt, zu erweitern und praktisch anwendbar zu machen. Diese Dissertation ist so strukturiert, dass ich in Kapitel 1 die Natur dynamischer Modelle bespreche, verschiedene Ansätze zum Umgang mit mehreren Personen betrachte und ein zeitkontinuierliches dynamisches Modell mit Input-Effekten (wie Interventionen) und einem Gaußschen Messmodell detailliert darstelle. In Kapitel 2 beschreibe ich die Verwendung der Software ctsem für R, die als Teil dieser Dissertation entwickelt wurde und die Modellierung von Strukturgleichungen und Mixed-Effects über einen frequentistischen Schätzansatz realisiert. In Kapitel 3 stelle ich einen hierarchischen, komplett Random-Effects beinhaltenden Bayesschen Schätzansatz vor, unter dem sich Personen nicht nur in Interceptparametern, sondern in allen Charakteristika von Mess - und Prozessmodell unterscheiden können, wobei die Schätzung individueller Parameter trotzdem von den Daten aller Personen profitiert. Kapitel 4 beschreibt die Verwendung der Bayesschen Erweiterung der Software ctsem. In Kapitel 5} betrachte ich die Natur experimenteller Interventionen vor dem Hintergrund zeitkontinuierlicher dynamischer Modellierung und zeige Ansätze, die die Art und Weise adressieren, mit der Interventionen auf psychologische Prozesse über die Zeit wirken. Das berührt Fragen, wie: 'Nach welcher Zeit zeigt eine Intervention ihre maximale Wirkung', 'Wie ändert sich die Form des Effektes im Laufe der Zeit' und 'Für wen ist die Wirkung am stärksten oder dauert am längsten an'. Viele Bei-spiele, die sowohl frequentistische als auch bayessche Formen der Software ctsem verwenden, sind enthalten. Im letzten Kapitel fasse ich die Dissertation zusammen, zeige Limitationen der angebotenen Ansätze auf und stelle meine Gedanken zu möglichen zukünftigen Entwicklungen dar. / With this dissertation I endeavor to extend, and make practically applicable for psychology, the statistical approach of continuous time dynamic modelling, in which the role of time is made explicit. The structure of this dissertation is such that in Chapter 1, I discuss the nature of dynamic models, consider various approaches to handling multiple subjects, and detail a continuous time dynamic model with input effects (such as interventions) and a Gaussian measurement model. In Chapter 2, I describe the usage of the ctsem software for R developed as part of this dissertation, which provides a frequentist, mixed effects, structural equation modelling approach to estimation. Chapter 3 details a hierarchical Bayesian, fully random effects approach to estimation, allowing for subjects to differ not only in intercept parameters but in all characteristics of the measurement and dynamic models -- while still benefiting from other subjects data for parameter estimation. Chapter 4 describes the usage of the Bayesian extension to the ctsem software. In Chapter 5 I consider the nature of experimental interventions in the continuous time dynamic modelling framework, and show approaches to address questions regarding the way interventions influence psychological processes over time, with questions such as 'how long does a treatment take to reach maximum effect', `how does the shape of the effect change over time', and 'for whom is the effect strongest, or longest lasting'. Many examples using both frequentist and Bayesian forms of the ctsem software are given. For the final chapter I summarise the dissertation, consider limitations of the approaches offered, and provide some thoughts on possible future developments.