The financial market is a stochastic and complex system that is challenging to model. It is crucial for investors to be able to model the probability of possible outcomes of financial investments and financing decisions in order to produce fruitful and productive investments. This study investigates how Monte Carlo simulations of random walks can be used to model the probability of future stock returns and how the simulations can be improved in order to provide better accuracy. The implemented method uses a mathematical model called Geometric Brownian Motion (GBM) in order to simulate stock prices. Ten Swedish large-cap stocks were used as a data set for the simulations, which in turn were conducted in time periods of 1 month, 3 months, 6 months, 9 months and 12 months. The two main parameters which determine the outcome of the simulations are the mean return of a stock and the standard deviation of historical returns. When these parameters were calculated without weights the method proved to be of no statistical significance. The method improved and thereby proved to be statistically significant for predictions for a 1 month time period when the parameters instead were weighted. By varying the assumptions regarding price distribution with respect to the size of the current time period and using other weights, the method could possibly prove to be more accurate than what this study suggests. Monte Carlo simulations seem to have the potential to become a powerful tool that can expand our abilities to predict and model stock prices. / Den finansiella marknaden är ett stokastiskt och komplext system som är svårt att modellera. Det är angeläget för investerare att kunna modellera sannolikheten för möjliga utfall av finansiella investeringar och beslut för att kunna producera fruktfulla och produktiva investeringar. Den här studien undersöker hur Monte Carlo-simuleringar av så kallade random walks kan användas för att modellera sannolikheten för framtida aktieavkastningar, och hur simuleringarna kan förbättras för att ge bättre precision. Den implementerade metoden använder den matematiska modellen Geometric Brownian Motion (GBM) för att simulera aktiepriser. Tio svenska large-cap aktier valdes ut som data för simuleringarna, som sedan gjordes för tidsperioderna 1 månad, 3 månader, 6 månader, 9 månader och 12 månader. Huvudparametrarna som bestämmer utfallet av simuleringarna är medelvärdet av avkastningarna för en aktie samt standardavvikelsen av de historiska avkastningarna. När dessa parametrar beräknades utan viktning gav metoden ingen statistisk signifikans. Metoden förbättrades och gav då statistisk signifikans på en 1 månadsperiod när parametrarna istället var viktade. Metoden skulle kunna visa sig ha högre precision än vad den här studien föreslår. Det är möjligt att till exempel variera antagandena angående prisernas fördelning med avseende på storleken av den nuvarande tidsperioden, och genom att använda andra vikter. Monte Carlo-simuleringar har därför potentialen att utvecklas till ett kraftfullt verktyg som kan öka vår förmåga att modellera och förutse aktiekurser.

Ce mémoire étudie le comportement des particules dont la position est maximale au temps t dans la marche aléatoire branchante et le mouvement brownien branchant sur R, pour des valeurs de t grandes. Plus exactement, on regarde le comportement du maximum d’une marche aléatoire branchante dans un environnement inhomogène en temps, au sens où la loi des accroissements varie en fonction du temps. On compare avec des modèles connus ou simplifiés, en particulier le modèle i.i.d., où l’on observe des marches aléatoires indépendantes et le modèle de la marche aléatoire homogène. On s’intéresse par la suite aux corrélations entre les particules maximales d’un mouvement brownien branchant. Plus précisément, on étudie le temps de branchement entre deux particules maximales. Finalement, on applique les méthodes et les résultats des premiers chapitres afin d’étudier les corrélations dans un mouvement brownien branchant dans un environnement inhomogène. Le résultat principal du mémoire stipule qu’il y a existence de temps de branchement au centre de l’intervalle [0, t] dans le mouvement brownien branchant inhomogène, ce qui n’est pas le cas pour le mouvement brownien branchant standard. On présentera également certaines simulations numériques afin de corroborer les résultats numériques et pour établir des hypothèses pour une recherche future. / This thesis studies the behavior of particles that are maximal at time t in branching random walk and branching Brownian motion on R, for large values of t. Precisely, we look at the behavior of the maximum in a branching random walk in a time-inhomogeneous environment, where the law of the increments varies with respect to time. We compare with known or simplified models such as the model where random walks are taken to be i.i.d. and the branching random walk in a time-homogeneous environment model. We then take a look at the correlations between maximal particles in a branching brownian motion. Specifically, we look at the branching time between those maximal particles. Finally, we apply results and methods from the first chapters to study those same correlations in branching Brownian motion in a inhomogeneous environment. The thesis’ main result establishes existence of branching time at the center of the interval [0, t] for the branching Brownian motion in a inhomogeneous environment, which is not the case for standard branching brownian motion.We also present results of simulations that agree with theoretical results and help establishing new hypotheses for future research.

La statistique des valeurs extrêmes est une question majeure dans divers contextes scientifiques. Cependant, bien que la description de la statistique d'un extremum global soit certainement une caractéristique importante, celle-ci ne se concentre que sur une seule variable parmi un grand nombre de variables aléatoires. Une question naturelle qui se pose alors est la suivante: ces valeurs extrêmes sont-elles isolées, loin des autres variables ou bien au contraire existe-t-il un grand nombre d'autres variables proches de ces valeurs extrêmes ? Ces questions ont suscité l'étude de la densité d'état de ces événements quasi-extrêmes. Il existe pour cette quantité peu de résultats pour des variables fortement corrélées, qui est pourtant le cas rencontré dans de nombreux modèles fondamentaux. Deux pistes de modèles physiques de variables fortement corrélées pouvant être étudiés analytiquement se démarquent alors: les positions d’une marche aléatoire et les valeurs propres de matrice aléatoire. Cette thèse est ainsi consacrée à l’étude de statistique d’extrêmes pour ces deux modèles de variables fortement corrélées. Dans une première partie, j’étudie le cas où la collection de variables aléatoires est la position au cours du temps d’un mouvement brownien, qui peut être contraint à être périodique, positif... Ce mouvement brownien est vu comme la limite d’un marcheur aléatoire classique après un grand nombre de pas. Il est alors possible d’interprèter ce problème comme celui d’une particule quantique dans un potentiel ce qui permet d’utiliser des méthodes puissantes issues de la mécanique quantique comme l’utilisation de propagateurs et de l’intégrale de chemin. Ces outils permettent de calculer la densité moyenne à partir du maximum pour les différents mouvements browniens contraints et même la distribution complète de cette quantité pour certains cas. Il est également possible de généraliser cette démarche à l’étude de plusieurs marches aléatoires indépendantes ou avec interaction. Cette démarche permet également d’effectuer une étude temporelle, ainsi que de généraliser à l’étude d’autres fonctionnelle du maximum. Dans la seconde partie, j’étudie le cas où la collection de variables aléatoires est composée des valeurs propres d’une matrice aléatoire. Ce travail se concentre sur l’études des matrices des ensembles gaussiens (GOE, GUE et GSE) ainsi qu’à l’étude des matrices de Wishart. L’étude du voisinage de la valeur propre maximale pour ces deux modèles est faite en utilisant une méthode fondée sur les propriétés des polynômes orthogonaux. Dans le cas des matrices gaussiennes unitaires GUE, j’ai obtenu une formule analytique pour la distribution à partir du maximum ainsi qu’une nouvelle expression de la statistique du gap entre les deux plus grandes valeurs propres en termes d’une fonction transcendante de Painlevé. Ces résultats, et plus particulièrement leurs généralisations aux cas GOE, sont alors appliqués à un modèle de verre de spin sphérique en champs moyen. Dans le cas des matrices de Wishart, l’analyse des polynômes orthogonaux dans le régime de double échelle m’a permis de retrouver les différentes statistiques de la valeur propre minimale et également de prouver une conjecture sur la première correction de taille finie pour des grandes matrices de la distribution de la valeur propre minimale dans la limite dite de «hard edge». / Extreme value statistics plays a keyrole in various scientific contexts. Although the description of the statistics of a global extremum is certainly an important feature, it focuses on the fluctuations of a single variable among many others. A natural question that arises is then the following: is this extreme value lonely at the top or, on the contrary, are there many other variables close to it ? A natural and useful quantity to characterize the crowding is the density of states near extremes. For this quantity, there exist very few exact results for strongly correlated variables, which is however the case encountered in many situations. Two physical models of strongly correlated variables have attracted much attention because they can be studied analytically : the positions of a random walker and the eigenvalues of a random matrix. This thesis is devoted to the study of the statistics near the maximum of these two ensembles of strongly correlated variables. In the first part, I study the case where the collection of random variables is the position of a Brownian motion, which may be constrained to be periodic or positive. This Brownian motion is seen as the limit of a classical random walker after a large number of steps. It is then possible to interpret this problem as a quantum particle in a potential which allows us to use powerful methods from quantum mechanics as propagators and path integral. These tools are used to calculate the average density from the maximum for different constrained Brownian motions and the complete distribution of this observable in certain cases. It is also possible to generalize this approach to the study of several random walks, independent or with interaction, as well as to the study of other functional of the maximum. In the second part, I study the case of the eigenvalues of random matrices, belonging to both Gaussian and Wishart ensembles. The study near the maximal eigenvalues for both models is performed using a method based on semi-classical orthogonal polynomials. In the case of Gaussian unitary matrices, I have obtained an analytical formula for the density near the maximum as well as a new expression for the distribution of the gap between the two largest eigenvalues. These results, and in particular their generalizations to different Gaussian ensembles, are then applied to the relaxational dynamics of a mean-field spin glass model. Finally, for the case of Wishart matrices I proposed a new derivation of the distribution of the smallest eigenvalue using orthogonal polynomials. In addition, I proved a conjecture on the first finite size correction of this distribution in the «hard edge» limit.

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.

Made available in DSpace on 2017-07-21T19:25:57Z (GMT). No. of bitstreams: 1 Romeu Szmoski.pdf: 12436289 bytes, checksum: 6e754552b30a293cd49e5368fbb3bfd0 (MD5) Previous issue date: 2009-03-03 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The Brownian motion is one important topic of the non-equilibrium statistical mechanics and it is related to many natural phenomena. The first observations and theories on this motion were essential for understand the microscopic behavior of the nature and its influence on macroscopics observables. In this dissertation, we studied the dynamics of a system composed of several interacting Brownian particle from the point of view of coupled maps lattices. We use a map with a direct correlation to the abovementioned motion and we employ four different kinds of couplings in order to represent several ways of interaction among the particles. Using nonlinear dynamics tools, we observe the situations in which the particles velocities synchronize or show a tendency to the synchronized state. We also obtain algebrics expressions for the Lyapunov spectra of lattices with regular couplings whose interactions decays with distance as a power-law and we raise two hypotheses about Lyapunov exponents of a lattice with the coupling probability decreasing with the distance, as follows: the exponents of this lattice converge to the exponents of the lattice whose interactions decay with the distance in agreement to a power-law when the number of particles is very large; and the Lyapunov exponents of this lattice are given by the sum of the probabilities products of the each coupling matrix by eigenvalues of these matrixes. The values obtained for the Lyapunov exponents by means of the expressions deducted are in agreement with those obtained by numerical approximations techniques. Regarding distributions of the velocities of the particles, we observed that occur an aproximation to a Gaussian distribuition when the intensity of the coupling tends to its maximum. / O movimento browniano e um dos assuntos mais intrigantes da mecanica estatıstica de nao-equilıbrio e explica uma serie de fenomenos observados na natureza. As primeiras observaçoes a respeito deste movimento e as teorias propostas para descreve-lo foram fundamentais para entender o comportamento microscópico da natureza e a influência deste sobre observáveis macroscópicos. Nesta dissertação, estudamos a dinâmica de um sistema composto por várias partículas brownianas interagentes a partir de modelos de redes de mapas acoplados. Utilizamos um mapa que possui uma correspondência física direta com o movimento mencionado e empregamos quatro formas distintas de acoplamentos a fim de representar as várias formas de interação entre as partículas. Por meio de ferramentas da dinâmica não ao linear, observamos as situações em que as velocidades das partículas sincronizam ou tendem para o estado sincronizado. Também em obtivemos expressões exatas para determinar os expoentes de Lyapunov das redes com acoplamentos regulares cujas interações decaem com a distância segundo uma lei de potência e levantamos duas hipóteses sobre os expoentes de Lyapunov de uma rede com probabilidade de acoplamento decaindo com a distância, a saber: que os expoentes desta rede convergem para os expoentes da rede cujas interações decaem com a distância segundo uma lei de potência quando o número de partículas é muito grande; e que os expoentes de Lyapunov desta rede são dados pela soma dos produtos da probabilidade de ocorrer cada matriz de acoplamento pelos respectivos autovalores destas matrizes. Os valores obtidos para os expoentes de Lyapunov por meio das expressões deduzidas mostraram-se em acordo com aqueles obtidos por técnicas de aproximações numéricas. Em relação às distribuições das velocidades das partículas, observamos que elas se aproximam de uma gaussiana quando a intensidade do acoplamento tende a seu valor máximo.

This thesis focusses on the properties of, and relationships between, several fundamental objects arising from critical physical models. In particular, we consider Schramm--Loewner evolutions, the Gaussian free field, Liouville quantum gravity and the Brownian continuum random tree. We begin by considering branching diffusions in a bounded domain $D\subset$ $R^{d}$, in which particles are killed upon hitting the boundary $\partial D$. It is known that such a system displays a phase transition in the branching rate: if it exceeds a critical value, the population will no longer become extinct almost surely. We prove that at criticality, under mild assumptions on the branching mechanism and diffusion, the genealogical tree associated with the process will converge to the Brownian CRT. Next, we move on to study Gaussian multiplicative chaos. This is the rigorous framework that allows one to make sense of random measures built from rough Gaussian fields, and again there is a parameter associated with the model in which a phase transition occurs. We prove a uniqueness and convergence result for approximations to these measures at criticality. From this point onwards we restrict our attention to two-dimensional models. First, we give an alternative, ``non-Gaussian" construction of Liouville quantum gravity (a special case of Gaussian multiplicative chaos associated with the 2-dimensional Gaussian free field), that is motivated by the theory of multiplicative cascades. We prove that the Liouville (GMC) measures associated with the Gaussian free field can be approximated using certain sequences of ``local sets" of the field. This is a particularly natural construction as it is both local and conformally invariant. It includes the case of nested CLE$_{4}$, when it is coupled with the GFF as its set of ``level lines". Finally, we consider this level line coupling more closely, now when it is between SLE$_{4}$ and the GFF. We prove that level lines can be defined for the GFF with a wide range of boundary conditions, and are given by SLE$_{4}$-type curves. As a consequence, we extend the definition of SLE$_{4}(\rho)$ to the case of a continuum of force points.

O estudo apresenta um modelo de precificação de derivativos financeiros baseado em processos de mistura de movimentos brownianos. A partir de uma modelagem probabilística, são apresentados ajustes ao modelo tradicional de Black-Scholes-Merton para contemplar situações em que o retorno do ativo-objeto não segue uma distribuição normal. O trabalho discute ainda um mecanismo de estimação de parâmetros da mistura de normais. O resultado da pesquisa possibilita a análise de preço de opções em situações mais gerais. / The study presents a model for pricing financial derivatives based on a mixture of Brownian motion processes. From a probabilistic modeling, the research focuses on adjustments to the traditional Black- Scholes- Merton model to address situations where the return of the underlying asset does not follow a normal distribution. The paper also discusses a mechanism to estimate parameters of a mixture of normal distributions. The result of the study allows an analysis of option price in more general situations.

Coating colors are applied to the base paper in order to maximize the performance of the end product. Coating colors are complex colloidal systems, mainly consisting of water, binders, and pigments. To understand the behavior of colloidal suspensions, an understanding of the interactions between its components is essential.

This thesis consists of two papers devoted to the asymptotics of random matrix ensembles and measure valued stochastic processes which can be considered as generalizations of the Gaussian unitary ensemble (GUE) of Hermitian matrices H=A+A†, where the entries of A are independent identically distributed (iid) centered complex Gaussian random variables. In the first paper, a system of interacting diffusing particles on the real line is studied; special cases include the eigenvalue dynamics of matrix-valued Ornstein-Uhlenbeck processes (Dyson's Brownian motion). It is known that the empirical measure process converges weakly to a deterministic measure-valued function and that the appropriately rescaled fluctuations around this limit converge weakly to a Gaussian distribution-valued process. For a large class of analytic test functions, explicit formulae are derived for the mean and covariance functionals of this fluctuation process. The second paper concerns a family of random matrix ensembles interpolating between the GUE and the Ginibre ensemble of n x n matrices with iid centered complex Gaussian entries. The asymptotic spectral distribution in these models is uniform in an ellipse in the complex plane, which collapses to an interval of the real line as the degree of non-Hermiticity diminishes. Scaling limit theorems are proven for the eigenvalue point process at the rightmost edge of the spectrum, and it is shown that a non-trivial transition occurs between Poisson and Airy point process statistics when the ratio of the axes of the supporting ellipse is of order n -1/3. / Denna avhandling består av två vetenskapliga artiklar som handlar om gränsvärdessatser för slumpmatriser och måttvärda stokastiska processer. De modeller som studeras kan betraktas som generaliseringar av den gaussiska unitära ensembeln (GUE) av hermiteska n x n-matriser H=A+A†, där A är en matris vars element är oberoende, likafördelade, centrerade, komplexa normalfördelade stokastiska variabler. I artikel I betraktas ett system av växelverkande diffunderande partiklar på reella linjen, vissa specialfall av denna modell kan tolkas som egenvärdesdynamiken för matrisvärda Ornstein-Uhlenbeck-processer (Dysons brownska rörelse). Sedan tidigare är det känt att den empiriska måttprocessen konvergerar svagt mot en deterministisk måttvärd funktion och att fluktuationerna runt denna gräns, i lämplig skalning, konvergerer svagt mot en distributionsvärd gaussisk process. För en stor klass av analytiska testfunktioner härleds explicita formler för medelvärdes- och kovariansfunktionalerna för denna fluktuationsprocess. Artikel II behandlar en familj av slumpmatrisensembler som interpolerar mellan GUE och Ginibre-ensembeln, bestående av matriser A som ovan. För denna modell är egenvärdena komplexa och asymptotiskt likformigt fördelade i en ellips i komplexa planet. Skalningsgränsvärdessatser för egenvärdet med maximal realdel och för egenvärdespunktprocessen kring detta visas för ett allmänt val av interpolationsparametern i modellen. Då förhållandet mellan axlarna i den asymptotiska ellipsen är av storleksordning n-1/3 uppträder en övergångsfas mellan Airypunktprocess- och Poissonprocessbeteendena, typiska för GUE respektive Ginibre-ensembeln. / QC 20100705