Custos de ajustamento e a dinâmica da estrutura de capital em empresas brasileiras

Diversos estudos de Finanças Corporativas consideram os custos associados aos ajustes da estrutura de capital das empresas irrelevantes tanto na forma quanto em magnitude. Este estudo analisou empiricamente a influência dos custos de ajustamento na dinâmica dos ajustes da estrutura de capital de empresas brasileiras de capital aberto no período de 1999 a 2007. A alavancagem foi abordada sob três diferentes cenários, considerando a presença de custos fixos, custos proporcionais e por uma composição de custos fixos e proporcionais através de simulações utilizando um modelo reduzido da estrutura de capital. Em seguida a análise não paramétrica da amostra revelou que as empresas apresentam um comportamento dinâmico em suas decisões de financiamento para o ajuste da estruturas de capital, mas que não se revelou contínuo. A utilização de um modelo de duration mostrou-se adequado para mensurar o intervalo de tempo entre os ajustes da estrutura de capital das empresas. Os resultados são extremamente relevantes e suportam a teoria de um comportamento de rebalanceamento dinâmico pelas empresas de suas estruturas de capital em torno de um intervalo ótimo. Entretanto os ajustes não ocorrem de forma imediata e a persistência de choques à estrutura de capital deve-se em sua maior parte aos custos associados aos ajustes do que a uma possível indiferença à estrutura de capital. . Este trabalho constitui-se como pioneiro no mercado brasileiro acerca dos custos de ajustamento da estrutura de capital e abre espaço para a discussão do comportamento ótimo em torno da estrutura de capital de empresas nacionais.

Fundos de investimento imobiliário no Brasil: as características que explicam o desempenho

O objetivo deste trabalho é ajudar o investidor que optou por investir seus recursos no mercado imobiliário a tomar sua decisão de investimento com base nas características endógenas facilmente identificáveis no prospecto dos Fundos de Investimento Imobiliários (FIIs). Foram selecionadas aquelas consideradas importantes pela literatura e foram construídos alguns modelos para testar sua influência na rentabilidade. Inicialmente, foi construído um modelo completo, com todas as variáveis, que apresentou resultados pouco relevantes, já que a maioria das variáveis não apresentou significância. Em seguida, um modelo reduzido foi montado com as variáveis que mais contribuíam para a rentabilidade, obtendo-se resultados relevantes. Através desse modelo, observou-se que FIIs que investem em desenvolvimento imobiliário, com foco no mercado residencial e com baixas taxas de administração, geraram maiores rentabilidades ao investidor.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties for a lack of parsimony, as well as the traditional ones. We suggest a new procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties. In order to compute the fit of each model, we propose an iterative procedure to compute the maximum likelihood estimates of parameters of a VAR model with short-run and long-run restrictions. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank, relative to the commonly used procedure of selecting the lag-length only and then testing for cointegration.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian inflation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in different measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian in ation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in di¤erent measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. A Monte Carlo study explores the finite sample performance of this procedure and evaluates the forecasting accuracy of models selected by this procedure. Two empirical applications confirm the usefulness of the model selection procedure proposed here for forecasting.

Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions

This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.