Neste trabalho, apresentamos uma extensão da análise bayesiana objetiva feita em Fonseca et al. (2008), baseada nas distribuições a priori de Jeffreys para o modelo de regressão linear com erros t-Student, para os quais consideramos a suposição de heteoscedasticidade. Mostramos que a distribuição a posteriori dos parâmetros do modelo regressão gerada pela distribuição a priori é própria. Através de um estudo de simulação, avaliamos as propriedades frequentistas dos estimadores bayesianos e comparamos os resultados com outras distribuições a priori encontradas na literatura. Além disso, uma análise de diagnóstico baseada na medida de divergência Kullback-Leiber é desenvolvida com a finalidade de estudar a robustez das estimativas na presença de observações atípicas. Finalmente, um conjunto de dados reais é utilizado para o ajuste do modelo proposto. / In this work , we present an extension of the objective bayesian analysis made in Fonseca et al. (2008), based on Jeffreys priors for linear regression models with Student t errors, for which we consider the heteroscedasticity assumption. We show that the posterior distribution generated by the proposed Jeffreys prior, is proper. Through simulation study , we analyzed the frequentist properties of the bayesian estimators obtained. Then we tested the robustness of the model through disturbances in the response variable by comparing its performance with those obtained under another prior distributions proposed in the literature. Finally, a real data set is used to analyze the performance of the proposed model . We detected possible in uential points through the Kullback -Leibler divergence measure, and used the selection model criterias EAIC, EBIC, DIC and LPML in order to compare the models.

Made available in DSpace on 2015-04-22T22:16:04Z (GMT). No. of bitstreams: 1 Nelson Lima de Souza Filho.pdf: 1556771 bytes, checksum: 33a38464a9de0ec3dca0da75c9c6b64e (MD5) Previous issue date: 2012-12-06 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / In regression models, the classical normal assumption for the distribution of the measurement errors is often violated, masking some important features of the variability of the data. Some practical actions to overcome this problem, like transformations of the data, sometimes are not effective. In this work we propose a methodology to overcome this problem, in the context of multivariate linear regression with measurement errors. In these models, the covariate is unobservable and the researcher observes a surrogate variable. These measurements are made with an additive error. We extend the classical normal model, by modeling jointly the covariate and the measurement errors by a finite mixture of densities which are in a general family, accommodating skewness, heavy tails and multi-modality at the same time, allowing a degree of flexibility that can not be met by the normal model. We proceed Bayesian inference through a Gibbs-type algorithm. Some proposed models are compared with existing symmetrical models, using a modified DIC criterion, through the analysis of simulated and real data. / Em modelos de regressão, o pressuposto clássico de normalidade para a distribuição dos erros aleatórios é muitas vezes violado, mascarando algumas características importantes da variabilidade dos dados. Algumas ações práticas para resolver esse problema, como transformações nos dados, revelam-se muitas vezes ineficazes. Neste trabalho apresentamos uma proposta para lidar com esta questão no contexto do modelo de regressão multivariada linear simples, quando a variável resposta e a variável regressora são observadas com erro aditivo o chamado modelo de regressão linear com erros nas variáveis. Em tais modelos, o pesquisador observa uma variável substituta em vez da covariável de interesse. Nós estendemos o modelo clássico normal, modelando a distribuição conjunta da covariável e dos erros aleatórios por uma mistura finita de densidades pertencentes a uma família de distribuições bem geral, acomodando ao mesmo tempo assimetria, caudas pesadas e multimodalidade, permitindo um grau de flexibilidade que não pode ser atingido pelo modelo normal. Para a parte de estimação desenvolvemos um algoritmo do tipo Gibbs para proceder estimação Bayesiana. Alguns modelos propostos foram comparados com modelos simétricos já existentes na literatura, utilizando um critério DIC modificado, através da análise de dados simulados e reais.

The role of risk management has gained momentum in recent years most notably after the recent financial crisis. This thesis uses a quantitative approach to evaluate the theory of value at risk which is considered a benchmark to measure financial risk. The thesis makes use of both parametric and non parametric approaches to evaluate the effectiveness of VAR as a standard tool in measuring risk of stock portfolio. This study uses the normal distribution, student t-distribution, historical simulation and the exponential weighted moving average at 95% and 99% confidence levels on the stock returns of Sonny Ericsson, Three Months Swedish Treasury bill (STB3M) and Nordea Bank. The evaluations of the VAR models are based on the Kupiec (1995) Test. From a general perspective, the results of the study indicate that VAR as a proxy of risk measurement has some imprecision in its estimates. However, this imprecision is not all the same for all the approaches. The results indicate that models which assume normality of return distribution display poor performance at both confidence levels than models which assume fatter tails or have leptokurtic characteristics. Another finding from the study which may be interesting is the fact that during the period of high volatility such as the financial crisis of 2008, the imprecision of VAR estimates increases. For the parametric approaches, the t-distribution VAR estimates were accurate at 95% confidence level, while normal distribution approach produced inaccurate estimates at 95% confidence level. However both approaches were unable to provide accurate estimates at 99% confidence level. For the non parametric approaches the exponentially weighted moving average outperformed the historical simulation approach at 95% confidence level, while at the 99% confidence level both approaches tend to perform equally. The results of this study thus question the reliability on VAR as a standard tool in measuring risk on stock portfolio. It also suggest that more research should be done to improve on the accuracy of VAR approaches, given that the role of risk management in today’s business environment is increasing ever than before. The study suggest VAR should be complemented with other risk measures such as Extreme value theory and stress testing, and that more than one back testing techniques should be used to test the accuracy of VAR.

Neste trabalho, apresentamos uma extensão da análise bayesiana objetiva feita em Fonseca et al. (2008), baseada nas distribuições a priori de Jeffreys para o modelo de regressão linear com erros t-Student, para os quais consideramos a suposição de heteoscedasticidade. Mostramos que a distribuição a posteriori dos parâmetros do modelo regressão gerada pela distribuição a priori é própria. Através de um estudo de simulação, avaliamos as propriedades frequentistas dos estimadores bayesianos e comparamos os resultados com outras distribuições a priori encontradas na literatura. Além disso, uma análise de diagnóstico baseada na medida de divergência Kullback-Leiber é desenvolvida com a finalidade de estudar a robustez das estimativas na presença de observações atípicas. Finalmente, um conjunto de dados reais é utilizado para o ajuste do modelo proposto. / In this work , we present an extension of the objective bayesian analysis made in Fonseca et al. (2008), based on Jeffreys priors for linear regression models with Student t errors, for which we consider the heteroscedasticity assumption. We show that the posterior distribution generated by the proposed Jeffreys prior, is proper. Through simulation study , we analyzed the frequentist properties of the bayesian estimators obtained. Then we tested the robustness of the model through disturbances in the response variable by comparing its performance with those obtained under another prior distributions proposed in the literature. Finally, a real data set is used to analyze the performance of the proposed model . We detected possible in uential points through the Kullback -Leibler divergence measure, and used the selection model criterias EAIC, EBIC, DIC and LPML in order to compare the models.

Submitted by Luciana Sebin (lusebin@ufscar.br) on 2016-09-26T18:57:40Z No. of bitstreams: 1 DissACRS.pdf: 1390452 bytes, checksum: a5365fdbf745228c0174f2643b3f7267 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-27T19:59:56Z (GMT) No. of bitstreams: 1 DissACRS.pdf: 1390452 bytes, checksum: a5365fdbf745228c0174f2643b3f7267 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-27T20:00:01Z (GMT) No. of bitstreams: 1 DissACRS.pdf: 1390452 bytes, checksum: a5365fdbf745228c0174f2643b3f7267 (MD5) / Made available in DSpace on 2016-09-27T20:00:08Z (GMT). No. of bitstreams: 1 DissACRS.pdf: 1390452 bytes, checksum: a5365fdbf745228c0174f2643b3f7267 (MD5) Previous issue date: 2016-02-18 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / In this work , we present an extension of the objective bayesian analysis made in Fonseca et al. (2008), based on Je reys priors for linear regression models with Student t errors, for which we consider the heteroscedasticity assumption. We show that the posterior distribution generated by the proposed Je reys prior, is proper. Through simulation study , we analyzed the frequentist properties of the bayesian estimators obtained. Then we tested the robustness of the model through disturbances in the response variable by comparing its performance with those obtained under another prior distributions proposed in the literature. Finally, a real data set is used to analyze the performance of the proposed model . We detected possible in uential points through the Kullback -Leibler divergence measure, and used the selection model criterias EAIC, EBIC, DIC and LPML in order to compare the models. / Neste trabalho, apresentamos uma extensão da análise bayesiana objetiva feita em Fonseca et al. (2008), baseada nas distribuicões a priori de Je reys para o modelo de regressão linear com erros t-Student, para os quais consideramos a suposicão de heteoscedasticidade. Mostramos que a distribuiçãoo a posteriori dos parâmetros do modelo regressão gerada pela distribuição a priori e própria. Através de um estudo de simulação, avaliamos as propriedades frequentistas dos estimadores bayesianos e comparamos os resultados com outras distribuições a priori encontradas na literatura. Além disso, uma análise de diagnóstico baseada na medida de divergência Kullback-Leiber e desenvolvida com analidade de estudar a robustez das estimativas na presença de observações atípicas. Finalmente, um conjunto de dados reais e utilizado para o ajuste do modelo proposto.

No description available.

This thesis proposes a new class of bivariate autoregressive conditional median duration models for matched high-frequency data and develops some inferential methods for an existing univariate model as well as the bivariate models introduced here to facilitate model fitting and forecasting. During the last two decades, the autoregressive conditional mean duration (ACD) model has been playing a dominant role in analyzing irregularly spaced high-frequency financial data. Univariate ACD models have been extensively discussed in the literature. However, some major challenges remain. The existing ACD models do not provide a good distributional fit to financial durations, which are right-skewed and often exhibit unimodal hazard rates. Birnbaum-Saunders (BS) distribution is capable of modeling a wide variety of positively skewed data. Median is not only a robust measure of central tendency, but also a natural scale parameter of the BS distribution. A class of conditional median duration models, the BS-ACD and the scale-mixture BS ACD models based on the BS, BS power-exponential and Student-t BS (BSt) distributions, have been suggested in the literature to improve the quality of the model fit. The BSt-ACD model is more flexible than the BS-ACD model in terms of kurtosis and skewness. In Chapter 2, we develop the maximum likelihood estimation method for the BSt-ACD model. The estimation is performed by utilizing a hybrid of optimization algorithms. The performance of the estimates is then examined through an extensive Monte Carlo simulation study. We also carry out model discrimination using both likelihood-based method and information-based criterion. Applications to real trade durations and comparison with existing alternatives are then made. The bivariate version of the ACD model has not received attention due to non-synchronicity. Although some bivariate generalizations of the ACD model have been introduced, they do not possess enough flexibility in modeling durations since they are conditional mean-based and do not account for non-monotonic hazard rates. Recently, the bivariate BS (BVBS) distribution has been developed with many desirable properties and characteristics. It allows for unimodal shapes of marginal hazard functions. In Chapter 3, upon using this bivariate BS distribution, we propose the BVBS-ACD model as a natural bivariate extension of the BS-ACD model. It enables us to jointly analyze matched duration series, and also capture the dependence between the two series. The maximum likelihood estimation of the model parameters and associated inferential methods have been developed. A Monte Carlo simulation study is then carried out to examine the performance of the proposed inferential methods. The goodness-of-fit and predictive performance of the model are also discussed. A real bivariate duration data analysis is provided to illustrate the developed methodology. The bivariate Student-t BS (BVBSt) distribution has been introduced in the literature as a robust extension of the BVBS distribution. It provides greater flexibility in terms of the kurtosis and skewness through the inclusion of an additional shape parameter. In Chapter 4, we propose the BVBSt-ACD model as a natural extension of the BSt-ACD model to the bivariate case. We then discuss the maximum likelihood estimation of the model parameters. A simulation study is carried out to investigate the performance of these estimators. Model discrimination is then done by using information-based criterion. Methods for evaluating the goodness-of-fit and predictive ability of the model are also discussed. A simulated data example is used to illustrate the proposed model as compared to the BVBS-ACD model. Finally, in Chapter 5, some concluding comments are made and also some problems for future research are mentioned. / Thesis / Master of Science (MSc)

O objetivo desta tese é estudar métodos de estimação baseados na função de verossimilhança em modelos mistos lineares elípticos. Derivamos inicialmente os métodos de máxima verossimilhança, máxima verossimilhança restrita e de máxima verossimilhança perfilada modificada para o modelo linear normal. Estendemos os métodos para os modelos lineares elípticos e encontramos diferenças entre as equações resultantes de cada método. A principal motivação deste trabalho é que o método de máxima verossimilhança restrita tem sido aplicado para obter estimadores menos viesados para os componentes de variância-covariância, em contraste com os estimadores de máxima verossimilhança. O método tem sido muito utilizado em modelos com estruturas de variância-covariância como é o caso dos modelos mistos lineares. Assim, procuramos estender o método para os modelos mistos lineares elípticos bem como comparar com outros procedimentos de estimação, máxima verossimilhança e máxima verossimilhança perfilada modificada. Estudamos em particular os modelos mistos lineares com erros t-Student e exponencial potência. / The aim of this thesis is to study estimation methods based on the likelihood functions in elliptical linear mixed models. First, we review the modified profile maximum likelihood and the restricted maximum likelihood methods as well as the traditional maximum likelihood method in normal linear models. Then, we extend the methodologies for elliptical linear models and we compare the estimating equations derived for each method. The main motivation of the work is that the restricted maximum likelihood method has been largely applied in normal linear mixed models in order to reduce the bias of the maximum likelihood variance-component estimators. So, we intend to investigate the possible extension for elliptical linear mixed models as well as to compare with the modified profile maximum likelihood and the maximum likelihood methods. Particular studies for Student-t and power exponential linear mixed models are presented.

No description available.

Neste trabalho, apresentamos os modelos simétricos parcialmente lineares AR(1), que generalizam os modelos parcialmente lineares para a presença de erros autocorrelacionados seguindo uma estrutura de autocorrelação AR(1) e erros seguindo uma distribuição simétrica ao invés da distribuição normal. Dentre as distribuições simétricas, podemos considerar distribuições com caudas mais pesadas do que a normal, controlando a curtose e ponderando as observações aberrantes no processo de estimação. A estimação dos parâmetros do modelo é realizada por meio do critério de verossimilhança penalizada, que utiliza as funções escore e a matriz de informação de Fisher, sendo todas essas quantidades derivadas neste trabalho. O número efetivo de graus de liberdade e resultados assintóticos também são apresentados, assim como procedimentos de diagnóstico, destacando-se a obtenção da curvatura normal de influência local sob diferentes esquemas de perturbação e análise de resíduos. Uma aplicação com dados reais é apresentada como ilustração. / In this master dissertation, we present the symmetric partially linear models with AR(1) errors that generalize the normal partially linear models to contain autocorrelated errors AR(1) following a symmetric distribution instead of the normal distribution. Among the symmetric distributions, we can consider heavier tails than the normal ones, controlling the kurtosis and down-weighting outlying observations in the estimation process. The parameter estimation is made through the penalized likelihood by using score functions and the expected Fisher information. We derive these functions in this work. The effective degrees of freedom and asymptotic results are also presented as well as the residual analysis, highlighting the normal curvature of local influence under different perturbation schemes. An application with real data is given for illustration.