Circuit Breaker no Brasil: histórico, efeitos no Índice Ibovespa e na volatilidade de ações
31/12/2020
Pedro Lucas Bastos Seixas.
Orientador: Walter Novaes.
Aqui você encontra as teses e dissertações defendidas, textos para discussão e produção acadêmica e de opinião de professores e alunos do Departamento de Economia.
A pesquisa pode ser feita por tipo de publicação, autor, título e período ou pela combinação deles. Os textos para discussão também podem ser pesquisados por número.
As monografias de Conclusão de Curso podem ser obtidas em http://www.maxwell.lambda.ele.puc-rio.br/.
31/12/2020
Pedro Lucas Bastos Seixas.
Orientador: Walter Novaes.
31/12/2020
Manuel Soares de Souza de Faria.
Orientador: Arthur Aguillar. Arthur Amorim Bragança.
31/12/2020
João Victor Teixeira Barros e Silva.
Orientador: Márcio Garcia.
31/12/2020
Maria Mittelbach Leite Santos.
Orientador: Maína Celidônio de Campos.
31/12/2020
Maurício Jackson Cardim Peres Silva.
Orientador: Arthur Amorim Bragança.
31/12/2020
Ralph Rangel Gazem Rufino.
Orientador: Márcio Garcia.
31/12/2020
Luiz Eduardo de A. M. Leão Teixeira.
Orientador: Marcelo Nuno Carneiro de Sousa.
31/12/2020
Alice Bergier Winograd.
Orientador: Tiago Couto Berriel.
31/12/2020
Beatriz Fikota de Sá Paixão.
Orientador: Sergio Besserman Vianna.
31/12/2020
Bernardo de Magalhães Pinto Gadelha.
Orientador: Leonardo Rezende.
31/12/2020
Francisco de Paulo Vicente de Siqueira Junior.
Orientador: Maria Elena Gava Reddo Alves.
31/12/2020
Isadora Spinola Pereira.
Orientador: Timo Hiller.
TD n. 680, 29/12/2020
There has been considerable advance in understanding the properties of sparse regularization procedures in high-dimensional models. In time series context, it is mostly restricted to Gaussian autoregressions or mixing sequences. We study oracle properties of LASSO estimation of weakly sparse vector-autoregressive models with heavy tailed, weakly dependent innovations with virtually no assumption on the conditional heteroskedasticity. In contrast to current literature, our innovation process satisfy an L1 mixingale type condition on the centered conditional covariance matrices. This condition covers L1-NED sequences and strong mixing sequences as particular examples. From a modeling perspective, it covers several multivariate-GARCH specifications, such as the BEKK model, and other factor stochastic volatility specifications that were ruled out by assumption in previous studies.
Ricardo Masini, Marcelo Medeiros, Eduardo F. Mendes.
TD n. 679, 28/12/2020
In this paper we survey the most recent advances in supervised machine learning and highdimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feed-forward and recurrent versions, and tree-based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of machine learning in economics and nance and provide an illustration with high-frequency nancial data
Ricardo Pereira Masini, Marcelo Medeiros, Eduardo F. Mendes.
16/11/2020
Breno Maurício Mattos Martins.
Orientador: Márcio Garcia.
Banca: Pablo Hector Seuanez Salgado. Walter Novaes.
06/11/2020
We provide a new way to select the regularization parameter for the LASSO and adaLASSO. It is based on the theory and incorporates an estimate of the variance of the noise. We show theoretical properties of the procedure and Monte Carlo simulations showing that it is able to handle more variables in the active set than other popular options for the regularization parameter.
Daniel Martins Coutinho.
Orientador: Marcelo Medeiros.
Banca: Ricardo Masini. Anders Kock.