M.Comm. / This study analyzes the effect of the subprime crisis on portfolio allocation from the perspective of dependence structure. Empirical evidence has proved that the multivariate normal distribution is inadequate to model portfolio asset return distribution - firstly because the empirical marginal distributions of asset returns are skewed and fat tailed; and secondly because it does not consider the possibility of extreme joint co-movement of asset returns (Fama and French, 1993; Richardson and Smith, 1993; Géczy, 1998; Longin and Solnik, 2001; Mashal and Zeevi, 2002). This study employs Archimedean copulas to capture both the dependence structure and the asymmetry of asset returns in the tails of the empirical distributions.

In this thesis we are interested in the calculation of economic capital for the to- tal loss which is the sum of partial dependent losses, whose dependence structure is described by Archimedean and hierarchical Archimedean copulas. Firstly, the concept of economic capital and the ways of its aggregation are introduced. Then the basic definitions and properties of copulas are listed, as well as the depen- dence measures. After that we work with definition and properties of Archimedean copulas and their simulation. We also mention the most popular families of Ar- chimedes copulas. Next, hierarchical Archimedean copulas are defined, as well as the algorithm for their sampling. Finally, we present methods for estimating the parameters of copulas and the recursive algorithm for estimating the hierarchical Archimedean copula structure. In the last chapter we perform simulation studies of selected models using hierarchical Archimedes copulas. 1

Šio darbo tema yra jungčių (angl. copulas) panaudojimas ryšiams tarp daugiamačių atsitiktinių dydžių modeliuoti. Jungtis yra funkcija, kuri sujungia kelių atsitiktinių dydžių marginalinius skirstinius į bendrą daugiamatę funkciją. Jungties sąvoka pirmą kartą statistikoje įvesta 1959 m. Šiame darbe aprašomos pagrindinės jungčių savybės, keletas jungčių šeimų, išskiriant atskirą šeimą - Archimedo jungtis, taip pat priklausomumo matai tarp atsitiktinių dydžių. Vėliau tinkamos jungties pritaikymo turimam duomenų rinkiniui procedūra iliustruojama nagrinėjant transporto priemonių valdytojų civilinės atsakomybės privalomojo draudimo žalų ir išlaidų žaloms administruoti duomenis. / In this Master work the concept of copulas as a tool for modeling relationships among multivariate outcomes is introduced. A copula is a function that links univariate margins to their multivariate distribution. Copulas were introduced in 1959. The literature on the statistical properties and application of copulas has been developing rapidly in recent years. In this Master work basic properties of copulas are described, then several families of copulas and relationships to measures of dependences. Later procedure for selecting the parametric family of Archimedean copulas is illustrated by using Lithuanian Motor Third Party Liability insurance data losses and expenses. For these data it is shown how to fit copulas according to nonparametric procedure which was proposed by Genest and Rivest.

[pt] A representação e interpretação claras da estrutura de dependência presente em vetores aleatórios, em particular em vetores bivariados, podem ser feitas com o uso do conceito de cópulas. Na análise bivariada, os coeficientes de dependência homogênea e heterogênea de cauda têm por objetivo estudar uma medida de dependência quando as variáveis assumem valores extre- mos. Obtemos as expressões dos coeficientes de dependência heterogênea de cauda a partir da função de distribuição acumulada condicional e apresen- tamos a demonstração de que os coeficientes de dependência homogênea de cauda de uma distribuição normal assimétrica são iguais a zero. Com o uso do conceito de cópulas e de dependência de cauda total, estudamos a estru- tura de dependência entre as seguintes variáveis: (i) log-retornos das taxas, interpoladas, para a estrutura a termo pré-fixada de 1 ano e de 2 anos; (ii) log-retorno das taxas para a estrutura a termo pré-fixada de 1 (um) ano e log-retorno do índice do Ibovespa; e (iii) log-retorno das taxas para a estru- tura a termo pré-fixada de 1 (um) ano e log-retorno da expectativa da taxa PTAX, 6 meses a frente. / [en] Using the concepts of copula we can represent and interpret the dependence structure presented in random vectors with clarity, particularly in bivariate vectors. In bivariate analysis, the role of both heterogeneous tail-dependence coefficient and homogenous tail- dependence coefficient are to study a measure of dependence when variables reach extreme values. We find expressions for the heterogeneous tail-dependence coefficients from the conditional cumulative distribution function and prove that the homoge- neous tail-dependence coefficients of a skewed normal distribution are equal to zero. Using the concepts of copula and the total tail dependence, we study the dependence structure between the following variables: (i) log- return of interpolated rates for the 1-year and 2-year fixed term structure; (ii) log-return of interpolated rate for the 1-year and log- return for the Bo- vespa index; e (iii) log-return of interpolated rate for the 1-year fixed term structure and log-return of expected PTAX, 6 months ahead.

Neste trabalho são apresentados modelos de regressão com respostas bivariadas obtidos através de funções cópulas. O objetivo de utilizar estes modelos bivariados é modelar a correlação entre eventos e captar nos modelos de regressão a influência da associação entre as variáveis resposta na presença de censura nos dados. Os parâmetros dos modelos, são estimados por meio dos métodos de máxima verossimilhança e jackknife. Alguns métodos de análise de sensibilidade como influência global, local e local total de um indivíduo, são introduzidos e calculados considerando diferentes esquemas de perturbação. Uma análise de resíduos foi proposta para verificar a qualidade do ajuste dos modelos utilizados e também foi proposta novas medidas de resíduos para respostas bivariadas. Métodos de simulação de Monte Carlo foram conduzidos para estudar a distribuição empírica dos resíduos marginais e bivariados propostos. Finalmente, os resultados são aplicados à dois conjuntos de dados dsponíveis na literatura. / In this work bivariate response regression models are presented with the use of copulas. The objective of this approach is to model the correlation between events and capture the influence of this correlation in the regression parameters. The models are used in the context of survival analysis and are ¯tted to two data sets available in the literature. Inferences are obtained using maximum likelihood and Jackknife methods. Sensitivity techniques such as local and global in°uence are proposed and calculated. A residual analysis is proposed to check the adequacy of the models and simulation methods are used to asses the empirical distribution of the marginal univariate and bivariate residual measures proposed.

Neste trabalho são apresentados modelos de regressão com respostas bivariadas obtidos através de funções cópulas. O objetivo de utilizar estes modelos bivariados é modelar a correlação entre eventos e captar nos modelos de regressão a influência da associação entre as variáveis resposta na presença de censura nos dados. Os parâmetros dos modelos, são estimados por meio dos métodos de máxima verossimilhança e jackknife. Alguns métodos de análise de sensibilidade como influência global, local e local total de um indivíduo, são introduzidos e calculados considerando diferentes esquemas de perturbação. Uma análise de resíduos foi proposta para verificar a qualidade do ajuste dos modelos utilizados e também foi proposta novas medidas de resíduos para respostas bivariadas. Métodos de simulação de Monte Carlo foram conduzidos para estudar a distribuição empírica dos resíduos marginais e bivariados propostos. Finalmente, os resultados são aplicados à dois conjuntos de dados dsponíveis na literatura. / In this work bivariate response regression models are presented with the use of copulas. The objective of this approach is to model the correlation between events and capture the influence of this correlation in the regression parameters. The models are used in the context of survival analysis and are ¯tted to two data sets available in the literature. Inferences are obtained using maximum likelihood and Jackknife methods. Sensitivity techniques such as local and global in°uence are proposed and calculated. A residual analysis is proposed to check the adequacy of the models and simulation methods are used to asses the empirical distribution of the marginal univariate and bivariate residual measures proposed.

Les copulas archimédiennes hiérarchiques ont récemment gagné en intérêt puisqu’elles généralisent la famille de copules archimédiennes, car elles introduisent une asymétrie partielle. Des algorithmes d’échantillonnages et des méthodes ont largement été développés pour de telles copules. Néanmoins, concernant l’estimation par maximum de vraisemblance et les tests d’adéquations, il est important d’avoir à disposition la densité de ces variables aléatoires. Ce travail remplie ce manque. Après une courte introduction aux copules et aux copules archimédiennes hiérarchiques, une équation générale sur les dérivées des noeuds et générateurs internes apparaissant dans la densité des copules archimédiennes hiérarchique. sera dérivée. Il en suit une formule tractable pour la densité des copules archimédiennes hiérarchiques. Des exemples incluant les familles archimédiennes usuelles ainsi que leur transformations sont présentés. De plus, une méthode numérique efficiente pour évaluer le logarithme des densités est présentée. / Nested Archimedean copulas recently gained interest since they generalize the well-known class of Archimedean copulas to allow for partial asymmetry. Sampling algorithms and strategies have been well investigated for nested Archimedean copulas. However, for likelihood based inference such as estimation or goodness-of-fit testing it is important to have the density. The present work fills this gap. After a short introduction on copula and nested Archimedean copulas, a general formula for the derivatives of the nodes and inner generators appearing in nested Archimedean copulas is developed. This leads to a tractable formula for the density of nested Archimedean copulas. Various examples including famous Archimedean families and transformations of such are given. Furthermore, a numerically efficient way to evaluate the log-density is presented.

Les copulas archimédiennes hiérarchiques ont récemment gagné en intérêt puisqu’elles généralisent la famille de copules archimédiennes, car elles introduisent une asymétrie partielle. Des algorithmes d’échantillonnages et des méthodes ont largement été développés pour de telles copules. Néanmoins, concernant l’estimation par maximum de vraisemblance et les tests d’adéquations, il est important d’avoir à disposition la densité de ces variables aléatoires. Ce travail remplie ce manque. Après une courte introduction aux copules et aux copules archimédiennes hiérarchiques, une équation générale sur les dérivées des noeuds et générateurs internes apparaissant dans la densité des copules archimédiennes hiérarchique. sera dérivée. Il en suit une formule tractable pour la densité des copules archimédiennes hiérarchiques. Des exemples incluant les familles archimédiennes usuelles ainsi que leur transformations sont présentés. De plus, une méthode numérique efficiente pour évaluer le logarithme des densités est présentée. / Nested Archimedean copulas recently gained interest since they generalize the well-known class of Archimedean copulas to allow for partial asymmetry. Sampling algorithms and strategies have been well investigated for nested Archimedean copulas. However, for likelihood based inference such as estimation or goodness-of-fit testing it is important to have the density. The present work fills this gap. After a short introduction on copula and nested Archimedean copulas, a general formula for the derivatives of the nodes and inner generators appearing in nested Archimedean copulas is developed. This leads to a tractable formula for the density of nested Archimedean copulas. Various examples including famous Archimedean families and transformations of such are given. Furthermore, a numerically efficient way to evaluate the log-density is presented.

Quantification of risks is one of the pillars of the contemporary insurance industry. Natural catastrophes and their modelling represents one of the most important areas of non-life insurance in the Czech Republic. One of the key inputs of catastrophe models is a spatial dependence structure in the portfolio of an insurance company. Copulas represents a more general view on dependence structures and broaden the classical approach, which is implicitly using the dependence structure of a multivariate normal distribution. The goal of this work, with respect to absence of comprehensive monographs in the Czech Republic, is to provide a theoretical basis for use of copulas. It focuses on general properties of copulas and specifics of two most commonly used families of copulas -- Archimedean and elliptical. The other goal is to quantify difference between the given copula and the classical approach, which uses dependency structure of a multivariate normal distribution, in modelled flood losses in the Czech Republic. Results are largely dependent on scale of losses in individual areas. If the areas have approximately a "tower" structure (i.e., one area significantly outweighs others), the effect of a change in the dependency structure compared to the classical approach is between 5-10% (up and down depending on a copula) at 99.5 percentile of original losses (a return period of once in 200 years). In case that all areas are approximately similarly distributed the difference, owing to the dependency structure, can be up to 30%, which means rather an important difference when buying the most common form of reinsurance -- an excess of loss treaty. The classical approach has an indisputable advantage in its simplicity with which data can be generated. In spite of having a simple form, it is not so simple to generate Archimedean copulas for a growing number of dimensions. For a higher number of dimensions the complexity of data generation greatly increases. For above mentioned reasons it is worth considering whether conditions of 2 similarly distributed variables and not too high dimensionality are fulfilled, before general forms of dependence are applied.