Volatilidade do mercado acionário e previsões da atividade econômica brasileira
30/06/2021
Eduarda Assis Ribeiro Schmidt.
Orientador: Marco Antonio Cavalcanti.
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30/06/2021
Eduarda Assis Ribeiro Schmidt.
Orientador: Marco Antonio Cavalcanti.
30/06/2021
Vitor Nakad Sterenberg.
Orientador: Yvan Becard.
30/06/2021
Victoria Castro Oliveira Rebelo Fioretti.
Orientador: Maria Cláudia Gomes Pereira Sarmiento Gutierrez.
30/06/2021
Miguel de Oliveira Castro Silbert.
Orientador: Maria Elena Gava Reddo Alves.
30/06/2021
Matheus Liberato Luz.
Orientador: Maria de Nazareth Maciel.
30/06/2021
Gustavo Tassara Pamplona.
Orientador: Sergio Besserman Vianna.
30/06/2021
Guilherme Gribel de Azevedo Ribeiro.
Orientador: Sergio Besserman Vianna.
30/06/2021
Francisco Backheuser Britto.
Orientador: Leonardo Rezende.
30/06/2021
Evandro Ferreira Rodrigues.
Orientador: Francisco de Lima Cavalcanti. Gustavo Gonzaga.
30/06/2021
Eduardo Harfouche de Andrade Dias.
Orientador: Maria Elena Gava Reddo Alves.
30/06/2021
Eduarda Franco Ferreira Höhn.
Orientador: Rafael Roos Guthmann.
30/06/2021
Bruno Tissenbaum Issler.
Orientador: Francisco de Lima Cavalcanti. Gustavo Gonzaga.
30/06/2021
Antonio Gabriel dos Santos Ferreira.
Orientador: Yvan Becard.
TD n. 684, 22/06/2021
We provide a new theory for nodewise regression when the residuals from a fitted factor model are used to apply our results to the analysis of maximum Sharpe ratio when the number of assets in a portfolio is larger than its time span. We introduce a new hybrid model where factor models are combined with feasible nodewise regression. Returns are generated from increasing number of factors plus idiosyncratic components (errors). The precision matrix of the idiosyncratic terms is assumed to be sparse, but the respective covariance matrix can be non-sparse. Since the nodewise regression is not feasible due to unknown nature of errors, we provide a feasible-residual based nodewise regression to estimate the precision matrix of errors, as a new method. Next, we show that the residual-based nodewise regression provides a consistent estimate for the precision matrix of errors. In another new development, we also show that the precision matrix of returns can be estimated consistently, even with increasing number of factors. Benefiting from the consistency of the precision matrix estimate of returns, we show that: (1) the portfolios in high dimensions are mean-variance efficient; (2) maximum out-of-sample Sharpe ratio estimator is consistent and the number of assets slows the convergence up to a logarithmic factor; (3) the maximum Sharpe ratio estimator is consistent when the portfolio weights sum to one; and (4) the Sharpe ratio estimators are consistent in global minimum-variance and mean-variance portfolios.
Mehmet Caner, Marcelo Medeiros, Gabriel F. R. Vasconcelos.
14/06/2021
Alexia Leite Barbosa.
Orientador: Sergio Besserman Vianna.
O Globo e O Estado de S. Paulo, 11/06/2021
Rogério Werneck.
TD n. 685, 30/05/2021
Price selection is a simple, model-free measure of selection in price setting and its contribution to inflation dynamics. It exploits comovement between inflation and the level from which adjusting prices departed. Prices that increase from lower-than-usual levels tend to push inflation above average. Using detailed micro-level consumer price data for the United Kingdom, the United States, and Canada, we nd robust evidence of strong price selection across goods and services. At a disaggregate level, price selection accounts for 37% of inflation variance in the United Kingdom, 36% in the United States, and 28% in Canada. Price selection is stronger for goods with less frequent price changes or with larger average price changes. Aggregate price selection is considerably weaker. A multisector sticky-price model accounts well for this evidence and demonstrates a monotone relationship between price selection and monetary non-neutrality. Revisto em maio 2021
Artigo aceito para publicação no Journal of Monetary Economics
Carlos Viana de Carvalho, Oleksiy Kryvtsov.