Este texto apresenta alguns dos elementos básicos envolvidos em um estudo introdutório das equações diferencias estocásticas. Tais equações modelam problemas a tempo contínuo em que as grandezas de interesse estão sujeitas a certos tipos de perturbações aleatórias. Em nosso estudo, a aleatoriedade nessas equações será representada por um termo que envolve o processo estocástico conhecido como Movimento Browniano. Para um tratamento matematicamente rigoroso dessas equações, faremos uso da Integral Estocástica de Itô. A construção dessa integral é um dos principais objetivos do texto. Depois de desenvolver os conceitos necessários, apresentaremos alguns exemplos e provaremos existência e unicidade de solução para equações diferenciais estocásticas satisfazendo certas hipóteses. / This text presents some of the basic elements involved in an introductory study of stochastic differential equations. Such equations describe certain kinds of random perturbations on continuous time models. In our study, the randomness in these equations will be represented by a term involving the stochastic process known as Brownian Motion. For a mathematically rigorous treatment of these equations, we use the Itô Stochastic Integral. The construction of this integral is one of the main goals of the text. After developing the necessary concepts, we present some examples and prove existence and uniqueness of solution of stochastic differential equations satisfying some hypothesis.

Este texto apresenta alguns dos elementos básicos envolvidos em um estudo introdutório das equações diferencias estocásticas. Tais equações modelam problemas a tempo contínuo em que as grandezas de interesse estão sujeitas a certos tipos de perturbações aleatórias. Em nosso estudo, a aleatoriedade nessas equações será representada por um termo que envolve o processo estocástico conhecido como Movimento Browniano. Para um tratamento matematicamente rigoroso dessas equações, faremos uso da Integral Estocástica de Itô. A construção dessa integral é um dos principais objetivos do texto. Depois de desenvolver os conceitos necessários, apresentaremos alguns exemplos e provaremos existência e unicidade de solução para equações diferenciais estocásticas satisfazendo certas hipóteses. / This text presents some of the basic elements involved in an introductory study of stochastic differential equations. Such equations describe certain kinds of random perturbations on continuous time models. In our study, the randomness in these equations will be represented by a term involving the stochastic process known as Brownian Motion. For a mathematically rigorous treatment of these equations, we use the Itô Stochastic Integral. The construction of this integral is one of the main goals of the text. After developing the necessary concepts, we present some examples and prove existence and uniqueness of solution of stochastic differential equations satisfying some hypothesis.

Le premier chapitre de cette thèse introduit les différentes notions que nous utiliserons et présente les travaux qui constituent ce mémoire.Dans le deuxième chapitre de cette thèse nous donnons une construction ainsi que les principales propriétés de l'intégrale stochastique par rapport au mBm harmonisable. Y sont également établies des formules d'Itô et une formule de Tanaka pour l'intégrale stochastique par rapport à ce mBm..Dans le troisième chapitre nous donnons une nouvelle définition, à la fois plus simple et plus générale, du mouvement brownien multifractionnaire. Nous montrons ensuite que le mBm apparaît naturellement comme limite de suite de somme de mouvement brownien fractionnaire (fBm) d’indices de Hurst différents.Nous appliquons alors cette idée pour tenter de construire une intégrale stochastique par rapport au mouvement brownien multifractionnaire à partir d’intégrales par rapport au fBm. Cela fait nous appliquons cette définition d’intégrale par rapport au mBm pour une méthode d’intégration donnée aux deux méthodes que sont le calcul de Malliavin et la théorie du bruit blanc.Dans ce dernier cas nous comparons alors l’intégrale ainsi construite à celle obtenue au chapitre 2. Le quatrième et dernier chapitre est une application du calcul stochastique développé dans les chapitres précédents. Nous y proposons un modèle à volatilité multifractionnaire où le processus de volatilité est dirigée par un mBm. L’intérêt résidant dans le fait que l’on peut ainsi prendre en compte à la fois la dépendance à long terme des accroissements de la volatilité mais aussi le fait que la trajectoire de ces accroissements varie au cours du temps.Utilisant alors la théorie de la quantification fonctionnelle pour, entre autres, approximer la solution de certaines des équations différentielles stochastiques, nous parvenons à calculer le prix d’option à départ forward et implicitons ainsi une nappe de volatilité que l’on représente graphiquement pour différentes maturités. / The aim of this PhD Thesis was to build and develop a stochastic calculus (in particular a stochastic integral) with respect to multifractional Brownian motion (mBm). Since the choice of the theory and the tools to use was not fixed a priori, we chose the White Noise theory which generalizes, in the case of fractional Brownian motion (fBm) , the Malliavin calculus. The first chapter of this thesis presents several notions we will use in the sequel.In the second chapter we present a construction as well as the main properties of stochastic integral with respect to harmonizable mBm.We also give Ito formulas and a Tanaka formula with respect to this mBm. In the third chapter we give a new definition, simplier and generalier of multifractional Brownian motion. We then show that mBm appears naturally as a limit of a sequence of fractional Brownian motions of different Hurst index.We then use this idea to build an integral with respect to mBm as a limit of sum of integrals with respect ot fBm. This being done we particularize this definition to the case of Malliavin calculus and White Noise theory. In this last case we compare the integral hence defined to the one we got in chapter 2. The fourth and last chapter propose a multifractional stochastic volatility model where the process of volatility is driven by a mBm. The interest lies in the fact that we can hence take into account, in the same time, the long range dependence of increments of volatility process and the fact that regularity vary along the time.Using the functional quantization theory in order to, among other things, approximate the solution of stochastic differential equations, we can compute the price of forward start options and then get and plot the implied volatility nappe that we graphically represent.

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Nesta dissertação desenvolvemos o Cálculo Estocástico para provar teoremas clássicos de Análise Complexa, em particular, o pequeno teorema de Picard. / In this dissertation we develop the Stochastic Calculus for to prove classical theorems in Complex Analysis, in particular, the little Picard\'s theorem.

Nesta dissertação desenvolvemos o Cálculo Estocástico para provar teoremas clássicos de Análise Complexa, em particular, o pequeno teorema de Picard. / In this dissertation we develop the Stochastic Calculus for to prove classical theorems in Complex Analysis, in particular, the little Picard\'s theorem.

Stochastic Integrals Driven by Isonormal Gaussian Processes and Applications Master Thesis - Petr Čoupek Abstract In this thesis, we introduce a stochastic integral of deterministic Hilbert space valued functions driven by a Gaussian process of the Volterra form βt = t 0 K(t, s)dWs, where W is a Brownian motion and K is a square integrable kernel. Such processes generalize the fractional Brownian motion BH of Hurst parameter H ∈ (0, 1). Two sets of conditions on the kernel K are introduced, the singular case and the regular case, and, in particular, the regular case is studied. The main result is that the space H of β-integrable functions can be, in the strictly regular case, embedded in L 2 1+2α ([0, T]; V ) which corresponds to the space L 1 H ([0, T]) for the fractional Brownian mo- tion. Further, the cylindrical Gaussian Volterra process is introduced and a stochastic integral of deterministic operator-valued functions, driven by this process, is defined. These results are used in the theory of stochastic differential equations (SDE), in particular, measurability of a mild solution of a given SDE is proven.

O aumento pelo interesse na teoria de integração estocástica é, basicamente, consequência da acirrada competição para entender, desenvolver e aplicar a matemática subjacente ao mercado mobiliário. Neste trabalho desenvolvemos, de maneira didática e visando aplicações, tal teoria. Para tanto, começamos apresentando um desenvolvimento cuidadoso da teoria dos martingais e dos principais resultados de medida e probabilidade relacionados. Depois apresentamos de maneira formal a teoria de integração estocástica com respeito aos semi-martingais contínuos. Finalizamos com um tratamento das principais aplicações dessa teoria como a fórmula de Itô, uma introdução às equações diferenciais estocásticas e a fórmula de Feynman-Kac. Apresentamos também, em um apêndice, a teoria de mudança de medida e o teorema de Girsanov. Tentamos durante o trabalho apresentar exemplos relacionados com finanças e ilustrar a importância do movimento Browniano. / The increasing interest in the theory of Stochastic Integration is due mainly to the competitive pressure to understand, develop and apply the underlying mathematics of security markets. In this work, we attempt to develop part of the theory in a didactical approach and focused toward some particular applications. For this purpose, we begin by introducing a thorough development of Martingale theory and the main related results on Measure and Probability theory. We then present in a formal way the Stochastic Integration Theory with respect to continuous Semimartingales. Subsequentially, we show some of the theory\'s main applications, such as Itô\'s formula, an introduction to the theory of Stochastic Differential Equations and Feynman-Kac\'s formula. We also present in the appendix Girsanov\'s theorem and a construction of Brownian motion. During the development of this text we endeavored to enrich it by including examples relevant to finance and emphasizing the importance of the ubiquitous Brownian motion.

O aumento pelo interesse na teoria de integração estocástica é, basicamente, consequência da acirrada competição para entender, desenvolver e aplicar a matemática subjacente ao mercado mobiliário. Neste trabalho desenvolvemos, de maneira didática e visando aplicações, tal teoria. Para tanto, começamos apresentando um desenvolvimento cuidadoso da teoria dos martingais e dos principais resultados de medida e probabilidade relacionados. Depois apresentamos de maneira formal a teoria de integração estocástica com respeito aos semi-martingais contínuos. Finalizamos com um tratamento das principais aplicações dessa teoria como a fórmula de Itô, uma introdução às equações diferenciais estocásticas e a fórmula de Feynman-Kac. Apresentamos também, em um apêndice, a teoria de mudança de medida e o teorema de Girsanov. Tentamos durante o trabalho apresentar exemplos relacionados com finanças e ilustrar a importância do movimento Browniano. / The increasing interest in the theory of Stochastic Integration is due mainly to the competitive pressure to understand, develop and apply the underlying mathematics of security markets. In this work, we attempt to develop part of the theory in a didactical approach and focused toward some particular applications. For this purpose, we begin by introducing a thorough development of Martingale theory and the main related results on Measure and Probability theory. We then present in a formal way the Stochastic Integration Theory with respect to continuous Semimartingales. Subsequentially, we show some of the theory\'s main applications, such as Itô\'s formula, an introduction to the theory of Stochastic Differential Equations and Feynman-Kac\'s formula. We also present in the appendix Girsanov\'s theorem and a construction of Brownian motion. During the development of this text we endeavored to enrich it by including examples relevant to finance and emphasizing the importance of the ubiquitous Brownian motion.