Tese (doutorado)—Universidade de Brasília, Universidade Federal da Paraíba, Universidade Federal do Rio Grande do Norte, Programa Multi-Institucional e Inter-Regional de Pós-Graduação em Ciências Contábeis, 2017. / Submitted by Raquel Almeida (raquel.df13@gmail.com) on 2018-02-27T16:30:48Z No. of bitstreams: 1 2017_AlineMouraCostadaSilva.pdf: 2061144 bytes, checksum: 7bbb473ff7ffbaef08720ac9941667bf (MD5) / Approved for entry into archive by Raquel Viana (raquelviana@bce.unb.br) on 2018-03-14T19:26:27Z (GMT) No. of bitstreams: 1 2017_AlineMouraCostadaSilva.pdf: 2061144 bytes, checksum: 7bbb473ff7ffbaef08720ac9941667bf (MD5) / Made available in DSpace on 2018-03-14T19:26:27Z (GMT). No. of bitstreams: 1 2017_AlineMouraCostadaSilva.pdf: 2061144 bytes, checksum: 7bbb473ff7ffbaef08720ac9941667bf (MD5) Previous issue date: 2018-03-14 / Esta tese teve por objetivo desenvolver um modelo econométrico estrutural para o mercado acionário brasileiro, de modo a explicar a determinação dos retornos de suas ações, por meio de uma modelagem denominada MIDAS. Para tal, foram utilizadas variáveis explanatórias que sintetizam as especificidades das empresas analisadas, assim como do ambiente econômico brasileiro. Com o propósito de realizar um teste de robustez do modelo MIDAS desenvolvido, um modelo de regressão convencional para dados em painel também foi estimado com as mesmas variáveis presentes naquele modelo. Posteriormente, buscou-se analisar as projeções dos retornos acionários desenvolvidas pelo modelo MIDAS, comparando-as com as projeções advindas do modelo convencional e da série histórica. Carteiras de ativos foram montadas com base no modelo MIDAS, ainda com o intuito de analisar as suas projeções. A amostra contemplou as instituições não financeiras listadas na BM&FBovespa (atual B3) e o período de análise compreendeu de 2010 a 2016. Os resultados indicaram que o modelo MIDAS desenvolvido nesta tese se mostrou robusto para a explicação e projeção dos retornos trimestrais das ações listadas no mercado acionário brasileiro, permitindo, inclusive, a construção de carteiras de ativos para investimento. Esse modelo superou o modelo convencional para dados em painel na explicação dos retornos acionários e, no que tange à projeção dos retornos das ações, o modelo MIDAS mostrou-se mais preciso estatisticamente do que a média histórica. Os resultados apresentados nesta tese reforçam a importância de estudos relacionados à modelagem dos retornos acionários em mercados emergentes, ao desenvolver um modelo robusto para a análise e a tomada de decisões de investimento no Brasil, o que corrobora para uma melhor compreensão e desenvolvimento de seu mercado acionário. / The purpose of this thesis was to develop a structural econometric model for the Brazilian stock market, in order to explain the determination of the returns of its shares, utilizing a model known as MIDAS. To accomplish that, explanatory variables that synthesize the fundamentals of the companies analyzed and other variables associated with the Brazilian economic environment were included. In order to perform a robustness test of the MIDAS model proposed, a conventional panel data regression model was also estimated with the same variables included in the first model. Subsequently, we sought to analyze stock return forecasts generated by the MIDAS model, by comparing them with forecasts generated by the conventional model and with the historical series as well. Asset portfolios were built based on the MIDAS model, also with the purpose of analyzing its forecasts. The sample includes the non-financial institutions listed on the BM&FBovespa (current B3) within the period comprised from 2010 to 2016. The results indicate that the MIDAS model developed in this thesis is robust for explaining and forecasting the quarterly returns of shares listed in the stock market including the construction of investment portfolios. This model overcomes the conventional panel data model in explaining stock returns and, regarding the forecasting of stock returns, the MIDAS model was also statistically more robust than the historical average. The results presented in this thesis strengthen the importance of studies related to the modeling of stock returns in emerging markets, by developing a robust model for investment analysis and decision-making in Brazil, which contributes to a better understanding and development of its stock market.

Potentially valuable information about the underlying data generating process of a dependent variable is often lost when an independent variable is transformed to fit into the same sampling frequency as a dependent variable. With the mixed data sampling (MIDAS) technique and increasingly available data at high frequencies, the issue of choosing an optimal sampling frequency becomes apparent. We use financial data and the MIDAS technique to estimate thousands of regressions and forecasts in the quarterly, monthly, weekly, and daily sampling frequencies. Model fit and forecast performance measurements are calculated from each estimation and used to generate summary statistics for each sampling frequency so that comparisons can be made between frequencies. Our regression models contain an autoregressive component and five additional independent variables and are estimated with varying lag length specifications that incrementally increase up to five years of lags. Each regression is used to forecast a rolling, one and two-step ahead, static forecast of the quarterly Yen and U.S Dollar spot exchange rate. Our results suggest that it may be favourable to include high frequency variables for closer modeling of the underlying data generating process but not necessarily for increased forecasting performance. / Graduate / 0501 / 0508 / 0511 / benjamincann@gmail.com