過去有相當多的文獻針對解約率建立模型，但由於資料取得之困難，鮮少文獻針對不同保單年度之解約率進行分析，本研究將以台灣壽險業資料分析不同保單年度之解約率行為，期望能找出解約率之期限結構，提供壽險業者訂價或風險管理之參考依據。 　　本研究使用台灣壽險業1987年至2011年間之生死合險及終身壽險資料，透過資料分析顯示兩險種之解約率關聯性不大，且應將繳別分為三類進行分析，分別為不分繳別、月繳及年繳和半年繳及季繳三類，針對各保單年度進行主成分分析，結果顯示皆需6至8個主成分方可達到90%之解釋力，並透過ARMA模型檢驗選定之主成分與總體經濟變數間之關聯性，進而觀察是否符合利率假說及緊急資金假說，最後透過VAR模型或ARMA模型模擬總體經濟變數和各主成分之分數，並利用主成分分析之結果將主成分分數轉換回保單年度變數，完成各保單年度解約率之模擬，建構出台灣壽險業解約率之期限結構。

Submitted by Aelson Maciera (aelsoncm@terra.com.br) on 2017-08-03T20:04:27Z No. of bitstreams: 1 TeseBSA.pdf: 10322083 bytes, checksum: 4c30c490934f23dbad9d5a1f087ef182 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-08T19:09:23Z (GMT) No. of bitstreams: 1 TeseBSA.pdf: 10322083 bytes, checksum: 4c30c490934f23dbad9d5a1f087ef182 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-08T19:09:30Z (GMT) No. of bitstreams: 1 TeseBSA.pdf: 10322083 bytes, checksum: 4c30c490934f23dbad9d5a1f087ef182 (MD5) / Made available in DSpace on 2017-08-08T19:15:39Z (GMT). No. of bitstreams: 1 TeseBSA.pdf: 10322083 bytes, checksum: 4c30c490934f23dbad9d5a1f087ef182 (MD5) Previous issue date: 2016-12-16 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Generalized autoregressive moving average (GARMA) models are a class of models that was developed for extending the univariate Gaussian ARMA time series model to a flexible observation-driven model for non-Gaussian time series data. This work presents the GARMA model with discrete distributions and application of resampling techniques to this class of models. We also proposed The Bayesian approach on GARMA models. The TGARMA (Transformed Generalized Autoregressive Moving Average) models was proposed, using the Box-Cox power transformation. Last but not least we proposed the Bayesian approach for the TGARMA (Transformed Generalized Autoregressive Moving Average). / Modelos Autoregressivos e de médias móveis generalizados (GARMA) são uma classe de modelos que foi desenvolvida para extender os conhecidos modelos ARMA com distribuição Gaussiana para um cenário de series temporais não Gaussianas. Este trabalho apresenta os modelos GARMA aplicados a distribuições discretas, e alguns métodos de reamostragem aplicados neste contexto. É proposto neste trabalho uma abordagem Bayesiana para os modelos GARMA. O trabalho da continuidade apresentando os modelos GARMA transformados, utilizando a transformação de Box-Cox. E por último porém não menos importante uma abordagem Bayesiana para os modelos GARMA transformados.

La plupart des modèles en statistique classique repose sur une hypothèse sur la distribution des données ou sur une distribution sous-jacente aux données. La validité de cette hypothèse permet de faire de l’inférence, de construire des intervalles de confiance ou encore de tester la fiabilité du modèle. La problématique des tests d’ajustement vise à s’assurer de la conformité ou de la cohérence de l’hypothèse avec les données disponibles. Dans la présente thèse, nous proposons des tests d’ajustement à la loi normale dans le cadre des séries chronologiques univariées et vectorielles. Nous nous sommes limités à une classe de séries chronologiques linéaires, à savoir les modèles autorégressifs à moyenne mobile (ARMA ou VARMA dans le cas vectoriel). Dans un premier temps, au cas univarié, nous proposons une généralisation du travail de Ducharme et Lafaye de Micheaux (2004) dans le cas où la moyenne est inconnue et estimée. Nous avons estimé les paramètres par une méthode rarement utilisée dans la littérature et pourtant asymptotiquement efficace. En effet, nous avons rigoureusement montré que l’estimateur proposé par Brockwell et Davis (1991, section 10.8) converge presque sûrement vers la vraie valeur inconnue du paramètre. De plus, nous fournissons une preuve rigoureuse de l’inversibilité de la matrice des variances et des covariances de la statistique de test à partir de certaines propriétés d’algèbre linéaire. Le résultat s’applique aussi au cas où la moyenne est supposée connue et égale à zéro. Enfin, nous proposons une méthode de sélection de la dimension de la famille d’alternatives de type AIC, et nous étudions les propriétés asymptotiques de cette méthode. L’outil proposé ici est basé sur une famille spécifique de polynômes orthogonaux, à savoir les polynômes de Legendre. Dans un second temps, dans le cas vectoriel, nous proposons un test d’ajustement pour les modèles autorégressifs à moyenne mobile avec une paramétrisation structurée. La paramétrisation structurée permet de réduire le nombre élevé de paramètres dans ces modèles ou encore de tenir compte de certaines contraintes particulières. Ce projet inclut le cas standard d’absence de paramétrisation. Le test que nous proposons s’applique à une famille quelconque de fonctions orthogonales. Nous illustrons cela dans le cas particulier des polynômes de Legendre et d’Hermite. Dans le cas particulier des polynômes d’Hermite, nous montrons que le test obtenu est invariant aux transformations affines et qu’il est en fait une généralisation de nombreux tests existants dans la littérature. Ce projet peut être vu comme une généralisation du premier dans trois directions, notamment le passage de l’univarié au multivarié ; le choix d’une famille quelconque de fonctions orthogonales ; et enfin la possibilité de spécifier des relations ou des contraintes dans la formulation VARMA. Nous avons procédé dans chacun des projets à une étude de simulation afin d’évaluer le niveau et la puissance des tests proposés ainsi que de les comparer aux tests existants. De plus des applications aux données réelles sont fournies. Nous avons appliqué les tests à la prévision de la température moyenne annuelle du globe terrestre (univarié), ainsi qu’aux données relatives au marché du travail canadien (bivarié). Ces travaux ont été exposés à plusieurs congrès (voir par exemple Tagne, Duchesne et Lafaye de Micheaux (2013a, 2013b, 2014) pour plus de détails). Un article basé sur le premier projet est également soumis dans une revue avec comité de lecture (Voir Duchesne, Lafaye de Micheaux et Tagne (2016)). / Several phenomena from natural and social sciences rely on distribution’s assumption among which the normal distribution is the most popular. The validity of that assumption is useful to setting up forecast intervals or for checking model adequacy of the underlying model. The goodness-of-fit procedures are tools to assess the adequacy of the data’s underlying assumptions. Autoregressive and moving average time series models are often used to find the mathematical behavior of these phenomena from natural and social sciences, and especially in the finance area. These models are based on some assumptions including normality distribution for the innovations. Normality assumption may be helpful for some testing procedures. Furthermore, stronger conclusions can be drawn from the adjusted model if the white noise can be assumed Gaussian. In this work, goodness-of-fit tests for checking normality for the innovations from autoregressive moving average time series models are proposed for both univariate and multivariate cases (ARMA and VARMA models). In our first project, a smooth test of normality for ARMA time series models with unknown mean based on a least square type estimator is proposed. We derive the asymptotic null distribution of the test statistic. The result here is an extension of the paper of Ducharme et Lafaye de Micheaux (2004), where they supposed the mean known and equal to zero. We use the least square type estimator proposed by Brockwell et Davis (1991, section 10.8) and we provide a rigorous proof that it is almost surely convergent. We show that the covariance matrix of the test is nonsingular regardless if the mean is known. We have also studied a data driven approach for the choice of the dimension of the family and we gave a finite sample approximation of the null distribution. Finally, the finite and asymptotic sample properties of the proposed test statistic are studied via a small simulation study. In the second project, goodness-of-fit tests for checking multivariate normality for the innovations from vector autoregressive moving average time series models are proposed. Since these time series models may rely on a large number of parameters, structured parameterization of the functional form is allowed. The methodology also relies on the smooth test paradigm and on families of orthonormal functions with respect to the multivariate normal density. It is shown that the smooth tests converge to convenient chi-square distributions asymptotically. An important special case makes use of Hermite polynomials, and in that situation we demonstrate that the tests are invariant under linear transformations. We observed that the test is not invariant under linear transformations with Legendre polynomials. A consistent data driven method is discussed to choose the family order from the data. In a simulation study, exact levels are studied and the empirical powers of the smooth tests are compared to those of other methods. Finally, an application to real data is provided, specifically on Canadian labour market data and annual global temperature. These works were exposed at several meeting (see for example Tagne, Duchesne and Lafaye de Micheaux (2013a, 2013b, 2014) for more details). A paper based on the first project is submitted in a refereed journal (see Duchesne, Lafaye de Micheaux et Tagne (2016)).

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Time series modeling and forecasting has many applicability in scientific and technological researchs. Specifically about variables restricted to the interval (0; 1), which includes rates and proportions, the classical regression models could not be suitable because they assume normality. In this context, Rocha and Cribari-Neto (2009) proposed the beta autoregressive moving average (βARMA) model. It admits that the variable of interest is beta distributed. The beta distribution is more flexible than the normal distribution and also assumes that de dependent variable is restricted to the interval (0; 1). Through βARMA is possible to obtain results closer to the nature of the data. But just choose the better parametric model does not guarantee the accuracy of the fitted model. To identify the lags is also relevant to ensure the accuracy of the adjusted model. It is in this purpose that the model selection criteria, or information criteria, were developed. They compare the explanatory capacity of a group of models and select, among this group, the model which minimizes the information loss. In this context, this paper aims to evaluate by Monte Carlo simulations the performance of different selection criteria in βARMA model. Considering several scenarios and sample sizes, the selection criteria evaluated was AIC, BIC, HQ, AICc, BICc and HQc. The results indicate that BICc, HQ and HQc had the better performance identifying the true model among the candidate models. Using the selection criteria indicated by the simulation study, were also adjusted βARMA models to real data. It were considered the credit delinquency and the relationship between payroll loan and individual credit, both variables are from national financial system. It was adjusted the classical ARIMA models too. This models were compared with βARMA in applications. For both variables was found a reasonable proximity between the original data and the predicted by the models, with advantage for βARMA, as much inside as outside the sample. / A modelagem e a previsão de séries temporais é um campo de ampla aplicabilidade em diversas áreas científicas e tecnológicas. No âmbito específico de variáveis restritas ao intervalo (0; 1), como taxas e proporções, a utilização de modelos clássicos, que supõem normalidade da variável de interesse, pode não ser adequada. Neste contexto, Rocha e Cribari-Neto (2009) propuseram o modelo beta autorregressivo de médias móveis (β ARMA). Por assumir que a variável de interesse possui distribuição beta, que é uma distribuição mais flexível que a normal e com suporte restrito ao intervalo (0; 1), o βARMA possibilita modelagens e previsões mais condizentes com a natureza desses dados. Contudo, apenas a escolha do modelo paramétrico mais adequado não garante a acurácia do modelo ajustado. A identificação das defasagens a serem incluídas também exerce um papel de relevância neste sentido. É neste propósito que foram desenvolvidos os critérios de seleção de modelos, ou critérios de informação. Estes comparam as capacidades de explicação entre um grupo de modelos candidatos e selecionam, dentro deste grupo, o modelo que minimiza a perda de informações. Diante do exposto, este trabalho tem o objetivo de avaliar, via simulações de Monte Carlo, o desempenho de diferentes critérios de seleção no modelo βARMA. Por meio de um extenso estudo de simulação, considerando diversos cenários e tamanhos amostrais, foram avaliados os desempenhos em amostras de tamanho finito dos critérios AIC, BIC, HQ, AICc, BICc e HQc. Como resultados numéricos gerais, destaca-se que os critérios HQ, BICc e HQc foram os que alcançaram os melhores níveis de identificação do modelo verdadeiro. Utilizando os critérios de seleção sugeridos no estudo de simulação também foram ajustados modelos βARMA a dados reais. Para isso, foram considerados o índice de inadimplência de crédito e a relação entre o crédito consignado e o crédito total pessoa física, ambos do Sistema Financeiro Nacional. Também foram ajustados os clássicos modelos ARIMA comparativamente ao modelo βARMA na realização de previsões e posterior comparação entre os resultados de ambas as aplicações. Para as duas variáveis há um grau razoável de proximidade entre os dados originais e previstos, com superioridade do βARMA tanto dentro quanto fora do conjunto de observações utilizado para estimação dos modelos.