To enhance test security of high stakes tests, it is vital to understand the way various exposure control strategies function under various IRT models. To that end the present dissertation focused on the performance of several exposure control strategies under the generalized partial credit model with an item pool of 100 and 200 items. These procedures are relatively easy to implement and have shown promise as an alternative to more complex exposure control strategies. Through unique algorithms these procedures select an item for administration from a subset of items in the item pool. The five procedures examined for efficacy were the modified within .10 logits, restricted modified within .10 logits, randomesque, restricted randomesque, and progressive restricted procedures. The modified within .10 logits, restricted modified within .10 logits, and randomesque, and restricted randomesque procedures select an item for administration from a subset of optimal items. To test the effect of the number of items available for selection in this subset, 3, 6, and 9 items were made available for selection in these procedures. Maximum information item selection was used as a base line, no exposure control, condition. The progressive restricted, restricted randomesque, and restricted modified within .10 logits procedures were found to optimally protect test security while not significantly degrading measurement precision. The restricted forms of the randomesque and modified within .10 logits procedures proved superior to their base procedures, particularly in controlling average maximum exposure rate. The incrementation of item group size in the modified within .10 logits, restricted modified within .10 logits, and randomesque, and restricted randomesque procedures demonstrated that increasing the item group size provided better test security while not significantly degrading measurement precision. Additionally, in general, the increase of the item pool size from 100 to 200 improved measurement precision and test security. Implications towards practical application are discussed and directions for future research are suggested. / text

Made available in DSpace on 2016-03-15T19:25:59Z (GMT). No. of bitstreams: 1 Fabio Yasuhiro Tsukahara.pdf: 1716038 bytes, checksum: fcb403b741576082c787fbd317d4e890 (MD5) Previous issue date: 2013-02-04 / The Basel II accord allows financial institutions to use internal models for measuring capital requirements. However, the use of internal models requires, among other approvals, a validation by an independent area. Thus, the development of techniques for validating risk models has gained much importance in the academic and in the financial markets. This study aims to evaluate some of the main techniques used for validate probability of default models, through the application of these techniques in PD models developed from simulated portfolios. Were evaluated traditional techniques such as KS, AR and area under ROC curve and newer techniques such as CIER, Information Value and measure M. The advantage of using simulated portfolios is that they allow the study of a large number of different situations, which would be impossible by using real portfolios. This study will provide an understanding of the limitations present in validation methodologies of PD models and an assessment of which techniques are more sensitive for each case analyzed. / O acordo de Basiléia II permite que instituições financeiras utilizem modelos internos para mensuração do capital mínimo exigido. No entanto, a utilização de modelos internos exige, dentre outras aprovações, a validação destes por uma área independente. Com isso, o desenvolvimento de técnicas para validação de modelos de riscos tem ganhado importância nos últimos anos tanto no âmbito acadêmico quanto no mercado financeiro. Este estudo tem como finalidade avaliar algumas das principais técnicas utilizadas na validação dos modelos de probabilidade de default (PD), através da aplicação destas técnicas em modelos desenvolvidos a partir de carteiras simuladas. Foram avaliadas desde técnicas mais tradicionais como KS, AR e área sob a curva ROC, até técnicas mais recentes como CIER, Information Value e a medida M. A vantagem em se utilizar carteiras simuladas é que elas permitem o estudo de um grande número de situações distintas, o que seria inviável através da utilização de carteiras reais. A partir deste estudo será possível entender as limitações das metodologias de validação dos modelos de PD e identificar quais técnicas são mais sensíveis a cada um dos casos analisados.

Submitted by Karen Pereira (karen.estat@gmail.com) on 2014-09-01T01:56:10Z No. of bitstreams: 1 Dissertação Final.pdf: 984140 bytes, checksum: 4ab727a73f17a49960bbcadba0a06b18 (MD5) / Approved for entry into archive by JOANA MARTORINI (joana.martorini@fgv.br) on 2014-09-01T12:29:21Z (GMT) No. of bitstreams: 1 Dissertação Final.pdf: 984140 bytes, checksum: 4ab727a73f17a49960bbcadba0a06b18 (MD5) / Made available in DSpace on 2014-09-01T12:42:12Z (GMT). No. of bitstreams: 1 Dissertação Final.pdf: 984140 bytes, checksum: 4ab727a73f17a49960bbcadba0a06b18 (MD5) Previous issue date: 2014-08-06 / Dado a importância da gestão de risco associada às operações de crédito, modelos estatísticos que avaliam o risco de inadimplência tornaram-se fundamentais na mensuração do risco associado a um cliente. Neste contexto, foi desenvolvido um modelo dinâmico de crédito utilizando variáveis características dos clientes, comportamentais e macroeconômicas através da Análise de Sobrevivência aplicada a dados discretos. Os resultados obtidos indicam que a inclusão de variáveis macroeconômicas provoca um efeito significativo, porém baixo, no ajuste do modelo. Entretanto, nota-se uma melhora expressiva no poder de previsão da taxa de inadimplência do portfólio quando aplicado a um conjunto de dados de validação. / Statistical models became fundamental in risk measuring associated with a client, mainly when the risk management had grown up his importance associated to credit transactions. In this context, a dynamic credit model was developed using client characteristics, behavioral and macroeconomic variables applying survival analysis to a discrete data. The results indicated the macroeconomics variables inclusion lead to a significant, but low effect in model fit. However, there is a significant improvement in the default rate predictive power of the portfolio when applied to a validation dataset.

Submitted by Guilherme da Silva Ferreira (guilhermesferreira053@gmail.com) on 2018-02-07T03:03:01Z No. of bitstreams: 1 MODELO DE PREVISÃO DE ENTRADA EM RECUPERAÇÃO JUDICIAL.pdf: 1209184 bytes, checksum: c2e3533402e1c78c0ddba7546a60c44b (MD5) / Approved for entry into archive by Thais Oliveira (thais.oliveira@fgv.br) on 2018-02-15T19:00:09Z (GMT) No. of bitstreams: 1 MODELO DE PREVISÃO DE ENTRADA EM RECUPERAÇÃO JUDICIAL.pdf: 1209184 bytes, checksum: c2e3533402e1c78c0ddba7546a60c44b (MD5) / Made available in DSpace on 2018-02-16T11:16:23Z (GMT). No. of bitstreams: 1 MODELO DE PREVISÃO DE ENTRADA EM RECUPERAÇÃO JUDICIAL.pdf: 1209184 bytes, checksum: c2e3533402e1c78c0ddba7546a60c44b (MD5) Previous issue date: 2017-12-08 / A crise observada na economia brasileira nos últimos anos chamou atenção para o direcionamento de crédito dos bancos e instituições financeiras, que reduziram o volume disponível para financiamento de curto e longo prazo liberados para as empresas. Diante deste cenário, modelos estatísticos se apresentam de suma importância com a função de ajudar na correta distribuição de créditos e redução de perdas com calotes. Neste contexto, foi desenvolvido um modelo para previsão de entrada em recuperação judicial utilizando informações contidas no balanço patrimonial das empresas e variáveis macroeconômicas através de regressão logística. Os resultados obtidos indicam que empresas com dificuldades financeiras são sensíveis à piora do cenário econômico. / The economic crisis observed in Brazil in the last years put a light in the form the banks and financial institutions direct their capital. The first reaction was reduction on loan volume and then, make better decision in the sense of for who make loan. In front of this scenario, statistic models are very important for help in the correct distribuition of credit and reduction of default. In this work, a model using logistic regression was builded to forecast the companies that have great chance to go bankrupt. The results showed that companies with financial stress are sensitive to the worsenig of the economic sceario.

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