Este estudo contempla dois ensaios em finanças quantitativas, relacionados, respectivamente, a modelos de apreçamento e risco sistêmico. No Capitulo 1, e apresentado uma alternativa para modelar opções multidimensionais, cujas estruturas de ganhos e perdas dependam das trajetórias dos processos dos preços dos ativos objetos. A modelagem sugerida considera os processos de Levy, uma classe de processos estocásticos bastante ampla, que permite a existência de saltos (descontinuidades) no processo dos preços dos ativos financeiros, e tem como caso particular o movimento Browniano. Para escrever a dependência entre os processos, os conceitos estáticos de copulas ordinárias são estendidos para o contexto dos processos de Levy, levando em consideração a medida de Levy, que caracteriza o comportamento dos saltos. São realizados estudos comparativos entre as copulas dinâmicas de Clayton e de Frank, no apreçamento dos contratos derivativos do tipo asiático, utilizando-se processos gama e técnicas de simulação de Monte Carlo. No Capitulo 2, a estrutura e dinâmica interbancária das exposições mutuas entre as instituições financeiras no Brasil e explorada bem como o capital destas reservas, utilizando um conjunto de dados únicos que considera vários períodos entre 2007 e 2008. Para isto e mostrado que a rede de exposições pode ser modelada adequadamente como um gráfico estocástico dirigido de escala - livre (ponderada) seguindo distribuições que apresentam caudas grossas. A relação entre as conexões das instituições financeiras e seu colchão-de-capital também são investigados neste estudo. Finalmente, a estrutura da rede e usada para explorar a extensão de risco sistêmico gerada no sistema individualmente pelas instituições financeiras. / This study comprises two essays in quantitative finance, related, respectively, to models in asset pricing and systemic risk. In Chapter 1, it is presented an alternative to modeling multidimensional options, where the pay-offs depend on the paths of the trajectories of the underlying-asset prices. The proposed technique considers Levy processes, a very ample class of stochastic processes that allows the existence of jumps (discontinuities) in the price process of financial assets, and as a particular case, comprises the Brownian motion. To describe the dependence among Levy processes, extending the static concepts of the ordinary copulas to the Levy processes context, considering the Levy measure, which characterizes the jumps behavior of these processes. A comparison between the Clayton and the Frank dynamic copulas and their impact in asset pricing of Asian type derivatives contracts is studied, considering gamma processes and Monte Carlo simulation procedures. In Chapter 2, the structure and dynamics of interbank exposures in Brazil using a unique data set of all mutual exposures of financial institutions in Brazil is explored, as well as their capital reserves, at various periods in 2007 and 2008. It is shown that the network of exposures can be adequately modeled as a directed scale-free (weighted) graph with heavy-tailed degree and weight distributions. The relation between connectivity of a financial institution and its capital buffer are also investigated in this study. Finally, the network structure is used to explore the extent of systemic risk generated in the system by the individual institutions.

We study certain questions related to the performance of the Karp-Sipser algorithm on the sparse Erdös-Rényi random graph. The Karp-Sipser algorithm, introduced by Karp and Sipser [34] is a greedy algorithm which aims to obtain a near-maximum matching on a given graph. The algorithm evolves through a sequence of steps. In each step, it picks an edge according to a certain rule, adds it to the matching and removes it from the remaining graph. The algorithm stops when the remining graph is empty. In [34], the performance of the Karp-Sipser algorithm on the Erdös-Rényi random graphs G(n,M = [^cn/₂]) and G(n, p = ^c/_n), c > 0 is studied. It is proved there that the algorithm behaves near-optimally, in the sense that the difference between the size of a matching obtained by the algorithm and a maximum matching is at most o(n), with high probability as n → ∞. The main result of [34] is a law of large numbers for the size of a maximum matching in G(n,M = ^cn/₂) and G(n, p = ^c/_n), c > 0. Aronson, Frieze and Pittel [2] further refine these results. In particular, they prove that for c < e, the Karp-Sipser algorithm obtains a maximum matching, with high probability as n → ∞; for c > e, the difference between the size of a matching obtained by the algorithm and the size of a maximum matching of G(n,M = ^cn/₂) is of order Θ_{log n}(n^1/5), with high probability as n → ∞. They further conjecture a central limit theorem for the size of a maximum matching of G(n,M = ^cn/₂) and G(n, p = ^c/_n) for all c > 0. As noted in [2], the central limit theorem for c < 1 is a consequence of the result of Pittel [45]. In this thesis, we prove a central limit theorem for the size of a maximum matching of both G(n,M = ^cn/₂) and G(n, p = ^c/_n) for c > e. (We do not analyse the case 1 ≤ c ≤ e). Our approach is based on the further analysis of the Karp-Sipser algorithm. We use the results from [2] and refine them. For c > e, the difference between the size of a matching obtained by the algorithm and the size of a maximum matching is of order Θ_{log n}(n^1/5), with high probability as n → ∞, and the study [2] suggests that this difference is accumulated at the very end of the process. The question how the Karp-Sipser algorithm evolves in its final stages for c > e, motivated us to consider the following problem in this thesis. We study a model for the destruction of a random network by fire. Let us assume that we have a multigraph with minimum degree at least 2 with real-valued edge-lengths. We first choose a uniform random point from along the length and set it alight. The edges burn at speed 1. If the fire reaches a node of degree 2, it is passed on to the neighbouring edge. On the other hand, a node of degree at least 3 passes the fire either to all its neighbours or none, each with probability 1/2. If the fire extinguishes before the graph is burnt, we again pick a uniform point and set it alight. We study this model in the setting of a random multigraph with N nodes of degree 3 and α(N) nodes of degree 4, where α(N)/N → 0 as N → ∞. We assume the edges to have i.i.d. standard exponential lengths. We are interested in the asymptotic behaviour of the number of fires we must set alight in order to burn the whole graph, and the number of points which are burnt from two different directions. Depending on whether α(N) » √N or not, we prove that after the suitable rescaling these quantities converge jointly in distribution to either a pair of constants or to (complicated) functionals of Brownian motion. Our analysis supports the conjecture that the difference between the size of a matching obtained by the Karp-Sipser algorithm and the size of a maximum matching of the Erdös-Rényi random graph G(n,M = ^cn/₂) for c > e, rescaled by n^1/5, converges in distribution.

Real-world large-scale complex networks such as the Internet, social networks and biological networks have increasingly attracted the interest of researchers from many areas. Accurate modelling of the statistical regularities of these large-scale networks is critical to understand their global evolving structures and local dynamical patterns. Traditionally, the Erdos and Renyi random graph model has helped the investigation of various homogeneous networks. During the past decade, a special computational methodology has emerged to study complex networks, the outcome of which is identified by two models: the Watts and Strogatz small-world model and the Barabasi-Albert scale-free model. At the core of the complex network modelling process is the extraction of characteristics of real-world networks. I have developed computer simulation algorithms for study of the properties of current theoretical models as well as for the measurement of two real-world complex networks, which lead to the isolation of three complex network modelling essentials. The main contribution of the thesis is the introduction and study of a new General Two-Stage growth model (GTS Model), which aims to describe and analyze many common-featured real-world complex networks. The tools we use to create the model and later perform many measurements on it consist of computer simulations, numerical analysis and mathematical derivations. In particular, two major cases of this GTS model have been studied. One is named the U-P model, which employs a new functional form of the network growth rule: a linear combination of preferential attachment and uniform attachment. The degree distribution of the model is first studied by computer simulation, while the exact solution is also obtained analytically. Two other important properties of complex networks: the characteristic path length and the clustering coefficient are also extensively investigated, obtaining either analytically derived solutions or numerical results by computer simulations. Furthermore, I demonstrate that the hub-hub interaction behaves in effect as the link between a network's topology and resilience property. The other is called the Hybrid model, which incorporates two stages of growth and studies the transition behaviour between the Erdos and Renyi random graph model and the Barabasi-Albert scale-free model. The Hybrid model is measured by extensive numerical simulations focusing on its degree distribution, characteristic path length and clustering coefficient. Although either of the two cases serves as a new approach to modelling real-world large-scale complex networks, perhaps more importantly, the general two-stage model provides a new theoretical framework for complex network modelling, which can be extended in many ways besides the two studied in this thesis.

In this work we provide several improvements in the study of phase transitions of interacting particle systems: - We determine a quantitative relation between non-extremality of the limiting Gibbs measure of a tree-based spin system, and the temporal mixing of the Glauber Dynamics over its finite projections. We define the concept of 'sensitivity' of a reconstruction scheme to establish such a relation. In particular, we focus on the independent sets model, determining a phase transition for the mixing time of the Glauber dynamics at the same location of the extremality threshold of the simple invariant Gibbs version of the model. - We develop the technical analysis of the so-called spatial mixing conditions for interacting particle systems to account for the connectivity structure of the underlying graph. This analysis leads to improvements regarding the location of the uniqueness/non-uniqueness phase transition for the independent sets model over amenable graphs; among them, the elusive hard-square model in lattice statistics, which has received attention since Baxter's solution of the analogous hard-hexagon in 1980. - We build on the work of Montanari and Gerschenfeld to determine the existence of correlations for the coloring model in sparse random graphs. In particular, we prove that correlations exist above the 'clustering' threshold of such a model; thus providing further evidence for the conjectural algorithmic 'hardness' occurring at such a point.

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Made available in DSpace on 2014-12-17T15:14:59Z (GMT). No. of bitstreams: 1 TiagoMV_DISSERT.pdf: 2054032 bytes, checksum: 01cc6c903e54670f8c4b846fab645cd0 (MD5) Previous issue date: 2012-02-24 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / This dissertation briefly presents the random graphs and the main quantities calculated from them. At the same time, basic thermodynamics quantities such as energy and temperature are associated with some of their characteristics. Approaches commonly used in Statistical Mechanics are employed and rules that describe a time evolution for the graphs are proposed in order to study their ergodicity and a possible thermal equilibrium between them / Esta disserta??o apresenta brevemente os grafos aleat?rios e as principais quantidades calculadas a partir deles. Ao mesmo tempo, grandezas b?sicas da Termodin?mica como energia e temperatura s?o associadas a algumas de suas caracter?sticas. Abordagens comumente utilizadas na Mec?nica Estat?stica s?o empregadas e regras que descrevem uma evolu??o temporal para os grafos s?o propostas com o objetivo de estudar sua ergodicidade e um poss?vel equil?brio t?rmico entre eles

Modelos e teorias científicas surgem da necessidade do homem entender melhor o funcionamento do mundo em que vive. Constantemente, novos modelos e técnicas são criados com esse objetivo. Uma dessas teorias recentemente desenvolvida é a da Criticalidade Auto-Organizada. No Capítulo 2 desta tese, apresentamos uma breve introdução a Criticalidade Auto-Organizada. Tendo a criticalidade auto-organizada como pano de fundo, no Capítulo 3, estudamos a dinâmica Bak-Sneppen (e diversas variantes) e a comparamos com alguns algoritmos de otimização. Apresentamos no Capítulo 4, uma revisão histórica e conceitual das redes complexas. Revisamos alguns importantes modelos tais como: Erdös-Rényi, Watts-Strogatz, de configuração e Barabási-Albert. No Capítulo 5, estudamos o modelo Barabási-Albert não-linear. Para este modelo, obtivemos uma expressão analítica para a distribuição de conectividades P(k), válida para amplo espectro do espaço de parâmetros. Propusemos também uma forma analítica para o coeficiente de agrupamento, que foi corroborada por nossas simulações numéricas. Verificamos que a rede Barabási-Albert não-linear pode ser assortativa ou desassortativa e que, somente no caso da rede Barabási-Albert linear, ela é não assortativa. No Capítulo 6, utilizando dados coletados do CD-ROM da revista Placar, construímos uma rede bastante peculiar -- a rede do futebol brasileiro. Primeiramente analisamos a rede bipartida formada por jogadores e clubes. Verificamos que a probabilidade de que um jogador tenha participado de M partidas decai exponencialmente com M, ao passo que a probabilidade de que um jogador tenha marcado G gols segue uma lei de potência. A partir da rede bipartida, construímos a rede unipartida de jogadores, que batizamos de rede de jogadores do futebol brasileiro. Nessa rede, determinamos várias grandezas: o comprimento médio do menor caminho e os coeficientes de agrupamento e de assortatividade. A rede de jogadores de futebol brasileiro nos permitiu analisar a evolução temporal dessas grandezas, uma oportunidade rara em se tratando de redes reais. / Models and scientific theories arise from the necessity of the human being to better understand how the world works. Driven by this purpose new models and techniques have been created. For instance, one of these theories recently developed is the Self-Organized Criticality, which is shortly introduced in the Chapter 2 of this thesis. In the framework of the Self-Organized Criticality theory, we investigate the standard Bak-Sneppen dynamics as well some variants of it and compare them with optimization algorithms (Chapter 3). We present a historical and conceptual review of complex networks in the Chapter 4. Some important models like: Erdös-Rényi, Watts-Strogatz, configuration model and Barabási-Albert are revised. In the Chapter 5, we analyze the nonlinear Barabási-Albert model. For this model, we got an analytical expression for the connectivity distribution P(k), which is valid for a wide range of the space parameters. We also proposed an exact analytical expression for the clustering coefficient which corroborates very well with our numerical simulations. The nonlinear Barabási-Albert network can be assortative or disassortative and only in the particular case of the linear Barabási-Albert model, the network is no assortative. In the Chapter 6, we used collected data from a CD-ROM released by the magazine Placar and constructed a very peculiar network -- the Brazilian soccer network. First, we analyzed the bipartite network formed by players and clubs. We find out that the probability of a footballer has played M matches decays exponentially with M, whereas the probability of a footballer to score G gols follows a power-law. From the bipartite network, we built the unipartite Brazilian soccer players network. For this network, we determined several important quantities: the average shortest path length, the clustering coefficient and the assortative coefficient. We were also able to analise the time evolution of these quantities -- which represents a very rare opportunity in the study of real networks.

Este estudo contempla dois ensaios em finanças quantitativas, relacionados, respectivamente, a modelos de apreçamento e risco sistêmico. No Capitulo 1, e apresentado uma alternativa para modelar opções multidimensionais, cujas estruturas de ganhos e perdas dependam das trajetórias dos processos dos preços dos ativos objetos. A modelagem sugerida considera os processos de Levy, uma classe de processos estocásticos bastante ampla, que permite a existência de saltos (descontinuidades) no processo dos preços dos ativos financeiros, e tem como caso particular o movimento Browniano. Para escrever a dependência entre os processos, os conceitos estáticos de copulas ordinárias são estendidos para o contexto dos processos de Levy, levando em consideração a medida de Levy, que caracteriza o comportamento dos saltos. São realizados estudos comparativos entre as copulas dinâmicas de Clayton e de Frank, no apreçamento dos contratos derivativos do tipo asiático, utilizando-se processos gama e técnicas de simulação de Monte Carlo. No Capitulo 2, a estrutura e dinâmica interbancária das exposições mutuas entre as instituições financeiras no Brasil e explorada bem como o capital destas reservas, utilizando um conjunto de dados únicos que considera vários períodos entre 2007 e 2008. Para isto e mostrado que a rede de exposições pode ser modelada adequadamente como um gráfico estocástico dirigido de escala - livre (ponderada) seguindo distribuições que apresentam caudas grossas. A relação entre as conexões das instituições financeiras e seu colchão-de-capital também são investigados neste estudo. Finalmente, a estrutura da rede e usada para explorar a extensão de risco sistêmico gerada no sistema individualmente pelas instituições financeiras. / This study comprises two essays in quantitative finance, related, respectively, to models in asset pricing and systemic risk. In Chapter 1, it is presented an alternative to modeling multidimensional options, where the pay-offs depend on the paths of the trajectories of the underlying-asset prices. The proposed technique considers Levy processes, a very ample class of stochastic processes that allows the existence of jumps (discontinuities) in the price process of financial assets, and as a particular case, comprises the Brownian motion. To describe the dependence among Levy processes, extending the static concepts of the ordinary copulas to the Levy processes context, considering the Levy measure, which characterizes the jumps behavior of these processes. A comparison between the Clayton and the Frank dynamic copulas and their impact in asset pricing of Asian type derivatives contracts is studied, considering gamma processes and Monte Carlo simulation procedures. In Chapter 2, the structure and dynamics of interbank exposures in Brazil using a unique data set of all mutual exposures of financial institutions in Brazil is explored, as well as their capital reserves, at various periods in 2007 and 2008. It is shown that the network of exposures can be adequately modeled as a directed scale-free (weighted) graph with heavy-tailed degree and weight distributions. The relation between connectivity of a financial institution and its capital buffer are also investigated in this study. Finally, the network structure is used to explore the extent of systemic risk generated in the system by the individual institutions.

Cette thèse aborde le problème de la recherche d'une structure (ou clustering) dans lesnoeuds d'un graphe. Dans le cadre des modèles aléatoires à variables latentes, on attribue à chaque noeud i une variable aléatoire non observée (latente) Zi, et la probabilité de connexion des noeuds i et j dépend conditionnellement de Zi et Zj . Contrairement au modèle d'Erdos-Rényi, les connexions ne sont pas indépendantes identiquement distribuées; les variables latentes régissent la loi des connexions des noeuds. Ces modèles sont donc hétérogènes, et leur structure est décrite par les variables latentes et leur loi; ce pourquoi on s'attache à en faire l'inférence à partir du graphe, seule variable observée.La volonté commune des deux travaux originaux de cette thèse est de proposer des méthodes d'inférence de ces modèles, consistentes et de complexité algorithmique au plus linéaire en le nombre de noeuds ou d'arêtes, de sorte à pouvoir traiter de grands graphes en temps raisonnable. Ils sont aussi tous deux fondés sur une étude fine de la distribution des degrés, normalisés de façon convenable selon le modèle.Le premier travail concerne le Stochastic Blockmodel. Nous y montrons la consistence d'un algorithme de classiffcation non supervisée à l'aide d'inégalités de concentration. Nous en déduisons une méthode d'estimation des paramètres, de sélection de modèles pour le nombre de classes latentes, et un test de la présence d'une ou plusieurs classes latentes (absence ou présence de clustering), et nous montrons leur consistence.Dans le deuxième travail, les variables latentes sont des positions dans l'espace ℝd, admettant une densité f, et la probabilité de connexion dépend de la distance entre les positions des noeuds. Les clusters sont définis comme les composantes connexes de l'ensemble de niveau t > 0 fixé de f, et l'objectif est d'en estimer le nombre à partir du graphe. Nous estimons la densité en les positions latentes des noeuds grâce à leur degré, ce qui permet d'établir une correspondance entre les clusters et les composantes connexes de certains sous-graphes du graphe observé, obtenus en retirant les nœuds de faible degré. En particulier, nous en déduisons un estimateur du nombre de clusters et montrons saconsistence en un certain sens / .This thesis addresses the clustering of the nodes of a graph, in the framework of randommodels with latent variables. To each node i is allocated an unobserved (latent) variable Zi and the probability of nodes i and j being connected depends conditionally on Zi and Zj . Unlike Erdos-Renyi's model, connections are not independent identically distributed; the latent variables rule the connection distribution of the nodes. These models are thus heterogeneous and their structure is fully described by the latent variables and their distribution. Hence we aim at infering them from the graph, which the only observed data.In both original works of this thesis, we propose consistent inference methods with a computational cost no more than linear with respect to the number of nodes or edges, so that large graphs can be processed in a reasonable time. They both are based on a study of the distribution of the degrees, which are normalized in a convenient way for the model.The first work deals with the Stochastic Blockmodel. We show the consistency of an unsupervised classiffcation algorithm using concentration inequalities. We deduce from it a parametric estimation method, a model selection method for the number of latent classes, and a clustering test (testing whether there is one cluster or more), which are all proved to be consistent. In the second work, the latent variables are positions in the ℝd space, having a density f. The connection probability depends on the distance between the node positions. The clusters are defined as connected components of some level set of f. The goal is to estimate the number of such clusters from the observed graph only. We estimate the density at the latent positions of the nodes with their degree, which allows to establish a link between clusters and connected components of some subgraphs of the observed graph, obtained by removing low degree nodes. In particular, we thus derive an estimator of the cluster number and we also show the consistency in some sense.

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