Orientador: Pedro Jose Catuogno / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Científica / Made available in DSpace on 2018-08-10T23:08:39Z (GMT). No. of bitstreams: 1 Almeida_LuisRobertoLucingerde_M.pdf: 590031 bytes, checksum: edf2150b0c1f63711d772e36d25e7e5f (MD5) Previous issue date: 2008 / Resumo: Nessa dissertação, demonstraremos uma fórmula de Itô para distribuições temperadas, ou seja, para uma distribuição F em S¿. Para tanto, apresentaremos a teoria das distribuições temperadas e do cálculo estocástico necessária para esta finalidade / Abstract: In this work, we will prove an Itô formula for tempered distributions, that is, for a distribution F in S0. To achieve this, we will introduce the theory of tempered distributions and of stochastic calculus that will be needed. / Mestrado / Analise Matematica / Mestre em Matemática

Cette thèse se compose de deux parties indépendantes qui portent sur le contrôle stochastique avec des espérances non linéaires et les équations stochastiques rétrogrades (EDSR), ainsi que sur les méthodes numériques de résolution de ces équations. Dans la première partie on étudie une nouvelle classe d'équations stochastiques rétrogrades, dont la particularité est que la condition terminale n'est pas fixée mais vérifie une contrainte non linéaire exprimée en termes de "f-espérances". Ce nouvel objet mathématique est étroitement lié aux problèmes de couverture approchée des options européennes où le risque de perte est quantifié en termes de mesures de risque dynamiques, induites par la solution d'une EDSR non linéaire. Dans le chapitre suivant on s'intéresse aux problèmes d'arrêt optimal pour les mesures de risque dynamiques avec sauts. Plus précisément, on caractérise dans un cadre markovien la mesure de risque minimale associée à une position financière comme l'unique solution de viscosité d'un problème d'obstacle pour une équation intégro-différentielle. Dans le troisième chapitre, on établit un principe de programmation dynamique faible pour un problème mixte de contrôle stochastique et d'arrêt optimal avec des espérances non linéaires, qui est utilisé pour obtenir les EDP associées.La spécificité de ce travail réside dans le fait que la fonction de gain terminal ne satisfait aucune condition de régularité (elle est seulement considérée mesurable), ce qui n'a pas été le cas dans la littérature précédente. Dans le chapitre suivant, on introduit un nouveau problème de jeux stochastiques, qui peut être vu comme un jeu de Dynkin généralisé (avec des espérances non linéaires). On montre que ce jeu admet une fonction valeur et on obtient des conditions suffisantes pour l'existence d'un point selle. On prouve que la fonction valeur correspond à l'unique solution d'une équation stochastique rétrograde doublement réfléchie avec un générateur non linéaire général. Cette caractérisation permet d'obtenir de nouveaux résultats sur les EDSR doublement réfléchies avec sauts. Le problème de jeu de Dynkin généralisé est ensuite étudié dans un cadre markovien.Dans la deuxième partie, on s'intéresse aux méthodes numériques pour les équations stochastiques rétrogrades doublement réfléchies avec sauts et barrières irrégulières, admettant des sauts prévisibles et totalement inaccessibles. Dans un premier chapitre, on propose un schéma numérique qui repose sur la méthode de pénalisation et l'approximation de la solution d'une EDSR par une suite d'EDSR discrètes dirigées par deux arbres binomiaux indépendants (un qui approxime le mouvement brownien et l'autre le processus de Poisson composé). Dans le deuxième chapitre, on construit un schéma en discrétisant directement l'équation stochastique rétrograde doublement réfléchie, schéma qui présente l'avantage de ne plus dépendre du paramètre de pénalisation. On prouve la convergence des deux schémas numériques et on illustre avec des exemples numériques les résultats théoriques. / This thesis consists of two independent parts which deal with stochastic control with nonlinear expectations and backward stochastic differential equations (BSDE), as well as with the numerical methods for solving these equations.We begin the first part by introducing and studying a new class of backward stochastic differential equations, whose characteristic is that the terminal condition is not fixed, but only satisfies a nonlinear constraint expressed in terms of "f - expectations". This new mathematical object is closely related to the approximative hedging of an European option, when the shortfall risk is quantified in terms of dynamic risk measures, induced by the solution of a nonlinear BSDE. In the next chapter we study an optimal stopping problem for dynamic risk measures with jumps.More precisely, we characterize in a Markovian framework the minimal risk measure associated to a financial position as the unique viscosity solution of an obstacle problem for partial integrodifferential equations. In the third chapter, we establish a weak dynamic programming principle for a mixed stochastic control problem / optimal stopping with nonlinear expectations, which is used to derive the associated PDE. The specificity of this work consists in the fact that the terminal reward does not satisfy any regularity condition (it is considered only measurable), which was not the case in the previous literature. In the next chapter, we introduce a new game problem, which can be seen as a generalized Dynkin game (with nonlinear expectations ). We show that this game admits a value function and establish sufficient conditions ensuring the existence of a saddle point . We prove that the value function corresponds to the unique solution of a doubly reected backward stochastic equation (DRBSDE) with a nonlinear general driver. This characterization allows us to obtain new results on DRBSDEs with jumps. The generalized Dynkin game is finally addressed in a Markovian framework.In the second part, we are interested in numerical methods for doubly reected BSDEs with jumps and irregular barriers, admitting both predictable and totally inaccesibles jumps. In the first chapter we provide a numerical scheme based on the penalisation method and the approximation of the solution of a BSDE by a sequence of discrete BSDEs driven by two independent random walks (one approximates the Brownian motion and the other one the compensated Poisson process). In the second chapter, we construct an alternative scheme based on the direct discretisation of the DRBSDE, scheme which presents the advantage of not depending anymore on the penalization parameter. We prove the convergence of the two schemes and illustrate the theoretical results with some numerical examples.

This paper surveys four distinct approaches to frontier estimation of multi-output (and simultaneously multi-input) technologies, when nothing but noisy quantity data are available. Parametrized distributions for inefficiency and noise are necessary for identification of inefficiency, when only cross-sectional data are available. In other respects suitable techniques may differ widely, as is shown. A final technique presented rigorously exploits the possibilities from panel-data by dropping parametrization of distributions as well as functional forms. It is illustrated how this last technique can be coupled with the others to provide a state-of-the-art estimation procedure for this setting. (author's abstract) / Series: Department of Economics Working Paper Series

In this work we present some new results concerning stochastic partial differential and integro-differential equations (SPDEs and SPIDEs) that appear in non-linear filtering. We prove existence and uniqueness of solutions of SPIDEs, we give a comparison principle and we suggest an approximation scheme for the non-local integral operators. Regarding SPDEs, we use techniques motivated by the work of De Giorgi, Nash, and Moser, in order to derive global and local supremum estimates, and a weak Harnack inequality.

The purpose of this thesis is to develop techniques for analysing interacting particle systems on the half-line. When the number of particles becomes large, stochastic partial differential equations (SPDEs) with Dirichlet boundary conditions will be the natural objects for describing the dynamics of the population's empirical measure. As a source of motivation, we consider systems that arise naturally as models for the pricing of portfolio credit derivatives, although similar applications are found in mathematical neuroscience, stochastic filtering and mean-field games. We will focus on a stochastic McKean--Vlasov system in which a collection of Brownian motions interact through a correlation which is a function of the proportion of particles that have been absorbed at level zero. We prove a law of large numbers where the limiting object is the unique solution to (the weak formulation of) the loss-dependent SPDE: dV_t(x) = 1/2 &part;_xxV_t(x)dt - p(L_t)&part;_xV_t(x)dW_t, V_t(0)=0, where L_t = 1-&lmoust;^&infin;<sub style='position: relative; left: -.8em;'>t</sub></sup>V_t(x)dx, V is a density process on the half-line and W is a Brownian motion. The correlation function is assumed to be piecewise Lipschitz, which encompasses a natural class of credit models. The first of our theoretical developments is to introduce the kernel smoothing method in the dual of the first Sobolev space, H^-1, with the aim of proving uniqueness results for SPDEs. A benefit of this approach is that only first order moment estimates of solutions are required, and in the particle setting this translates into studying the particles at an individual level rather than as a correlated collection. The second idea is to extend Skorokhod's M₁ topology to the space of processes that take values in the tempered distributions. The benefit we gain is that monotone functions have zero modulus of continuity under this topology, so the loss process, L, is easy to control. As a final example, we consider the fluctuations in the convergence of a basic particle system with constant correlation. This gives rise to a central limit theorem, for which the limiting object is a solution to an SPDE with random transport and an additive idiosyncratic driver acting on the first derivative terms. Conditional on the systemic random variables, this driver is a space-time white noise with intensity controlled by the empirical measure of the underlying system. The SPDE has insufficient regularity for us to work in any Sobolev space higher than H^-1, hence we have an example of where our extension to the kernel smoothing method is necessary.

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This thesis solves estimation and control problems in computational neuroscience, mathematically dealing with the first-passage times of diffusion stochastic processes. We first derive estimation algorithms for model parameters from first-passage time observations, and then we derive algorithms for the control of first-passage times. Finally, we solve an optimal design problem which combines elements of the first two: we ask how to elicit first-passage times such as to facilitate model estimation based on said first-passage observations. The main mathematical tools used are the Fokker-Planck partial differential equation for evolution of probability densities, the Hamilton-Jacobi-Bellman equation of optimal control and the adjoint optimization principle from optimal control theory. The focus is on developing computational schemes for the solution of the problems. The schemes are implemented and are tested for a wide range of parameters.

We introduce computationally efficient Monte Carlo methods for studying the statistics of stochastic differential equations in two distinct settings. In the first, we derive importance sampling methods for data assimilation when the noise in the model and observations are small. The methods are formulated in discrete time, where the "posterior" distribution we want to sample from can be analyzed in an accessible small noise expansion. We show that a "symmetrization" procedure akin to antithetic coupling can improve the order of accuracy of the sampling methods, which is illustrated with numerical examples. In the second setting, we develop "stochastic continuation" methods to estimate level sets for statistics of stochastic differential equations with respect to their parameters. We adapt Keller's Pseudo-Arclength continuation method to this setting using stochastic approximation, and generalized least squares regression. Furthermore, we show that the methods can be improved through the use of coupling methods to reduce the variance of the derivative estimates that are involved.

The dynamical origin of complexity is an object of intense debate and, up to moment of writing this manuscript, no unified approach exists as to how it should be properly addressed. This research work adopts the perspective of complexity as characterized by the emergence of non-Poisson renewal processes. In particular I introduce two new complex system models, namely the two-state stochastic clocks and the integrate-and-fire stochastic neurons, and investigate its coupled dynamics in different network topologies. Based on the foundations of renewal theory, I show how complexity, as manifested by the occurrence of non-exponential distribution of events, emerges from the interaction of the units of the system. Conclusion is made on the work's applicability to explaining the dynamics of blinking nanocrystals, neuron interaction in the human brain, and synchronization processes in complex networks.

Renewal theory began development in the early 1940s, as the need for it in the industrial engineering sub-discipline operations research had risen. In time, the theory found applications in many stochastic processes. In this thesis I investigated the effect of seasonal effects on Poisson and non-Poisson renewal processes in the form of perturbations. It was determined that the statistical analysis methods developed at UNT Center for Nonlinear Science can be used to detect the effects of seasonality on the data obtained from Poisson/non-Poisson renewal systems. It is proved that a perturbed Poisson process can serve as a paradigmatic model for a case where seasonality is correlated to the noise and that diffusion entropy method can be utilized in revealing this relation. A renewal model making a connection with the stochastic resonance phenomena is used to analyze a previous neurological experiment, and it was shown that under the effect of a nonlinear perturbation, a non-Poisson system statistics may make a transition and end up in the of Poisson basin of statistics. I determine that nonlinear perturbation of the power index for a complex system will lead to a change in the complexity characteristics of the system, i.e., the system will reach a new form of complexity.