Especificação de um modelo para explicação e projeção de retornos do IBRX-100

Neste trabalho, propomos uma especificação de modelo econométrico na forma reduzida, estimado por mínimos quadrados ordinários (MQO) e baseado em variáveis macroeconômicas, com o objetivo de explicar os retornos trimestrais do índice de ações IBRX-100, entre 2001 e 2015. Testamos ainda a eficiência preditiva do modelo e concluímos que o erro de previsão estimado em janela móvel, com re-estimação de MQO a cada rodada, e utilização de VAR auxiliar para projeção dos regressores, é significativamente inferior ao erro de previsão associado à hipótese de Random Walk para o horizonte de previsão de um trimestre a frente.

Brazilian international and inter-state trade flows: an exploratory analysis using the gravity model

Recent efforts toward a world with freer trade, like WTO/GATT or regional Preferential Trade Agreements(PTAs), were put in doubt after McCallum's(1995) finding of a large border effect between US and Canadian provinces. Since then, there has been a great amount of research on this topic employing the gravity equation. This dissertation has two goals. The first goal is to review comprehensively the recent literature about the gravity equation, including its usages, econometric specifications, and the efforts to provide it with microeconomic foundations. The second goal is the estimation of the Brazilian border effect (or 'home-bias trade puzzle') using inter-state and international trade flow data. It is used a pooled cross-section Tobit model. The lowest border effect estimated was 15, which implies that Brazilian states trade among themselves 15 times more than they trade with foreign countries. Further research using industry disaggregated data is needed to qualify the estimated border effect with respect to which part of that effect can be attributed to actual trade costs and which part is the outcome of the endogenous location problem of the firm.

An analysis of quit and dismissal determinants between 1988 and 1999 using the bivariate probit model

Excessive labor turnover may be considered, to a great extent, an undesirable feature of a given economy. This follows from considerations such as underinvestment in human capital by firms. Understanding the determinants and the evolution of turnover in a particular labor market is therefore of paramount importance, including policy considerations. The present paper proposes an econometric analysis of turnover in the Brazilian labor market, based on a partial observability bivariate probit model. This model considers the interdependence of decisions taken by workers and firms, helping to elucidate the causes that lead each of them to end an employment relationship. The Employment and Unemployment Survey (PED) conducted by the State System of Data Analysis (SEADE) and by the Inter-Union Department of Statistics and Socioeconomic Studies (DIEESE) provides data at the individual worker level, allowing for the estimation of the joint probabilities of decisions to quit or stay on the job on the worker’s side, and to maintain or fire the employee on the firm’s side, during a given time period. The estimated parameters relate these estimated probabilities to the characteristics of workers, job contracts, and to the potential macroeconomic determinants in different time periods. The results confirm the theoretical prediction that the probability of termination of an employment relationship tends to be smaller as the worker acquires specific skills. The results also show that the establishment of a formal employment relationship reduces the probability of a quit decision by the worker, and also the firm’s firing decision in non-industrial sectors. With regard to the evolution of quit probability over time, the results show that an increase in the unemployment rate inhibits quitting, although this tends to wane as the unemployment rate rises.

The impact of OFDI on economic growth countries. An econometric approach using panel data and time-series evidence

The thesis at hand adds to the existing literature by investigating the relationship between economic growth and outward foreign direct investments (OFDI) on a set of 16 emerging countries. Two different econometric techniques are employed: a panel data regression analysis and a time-series causality analysis. Results from the regression analysis indicate a positive and significant correlation between OFDI and economic growth. Additionally, the coefficient for the OFDI variable is robust in the sense specified by the Extreme Bound Analysis (EBA). On the other hand, the findings of the causality analysis are particularly heterogeneous. The vector autoregression (VAR) and the vector error correction model (VECM) approaches identify unidirectional Granger causality running either from OFDI to GDP or from GDP to OFDI in six countries. In four economies causality among the two variables is bidirectional, whereas in five countries no causality relationship between OFDI and GDP seems to be present.

Analysis of contagion from the constant conditional correlation model with Markov regime switching

Over the last decades, the analysis of the transmissions of international nancial events has become the subject of many academic studies focused on multivariate volatility models volatility. The goal of this study is to evaluate the nancial contagion between stock market returns. The econometric approach employed was originally presented by Pelletier (2006), named Regime Switching Dynamic Correlation (RSDC). This methodology involves the combination of Constant Conditional Correlation Model (CCC) proposed by Bollerslev (1990) with Markov Regime Switching Model suggested by Hamilton and Susmel (1994). A modi cation was made in the original RSDC model, the introduction of the GJR-GARCH model formulated in Glosten, Jagannathan e Runkle (1993), on the equation of the conditional univariate variances to allow asymmetric e ects in volatility be captured. The database was built with the series of daily closing stock market indices in the United States (SP500), United Kingdom (FTSE100), Brazil (IBOVESPA) and South Korea (KOSPI) for the period from 02/01/2003 to 09/20/2012. Throughout the work the methodology was compared with others most widespread in the literature, and the model RSDC with two regimes was de ned as the most appropriate for the selected sample. The set of results provide evidence for the existence of nancial contagion between markets of the four countries considering the de nition of nancial contagion from the World Bank called very restrictive. Such a conclusion should be evaluated carefully considering the wide diversity of de nitions of contagion in the literature.

Simulation-based smoothing and filtering in factor stochastic volatility models : two econometric applications

In this article we use factor models to describe a certain class of covariance structure for financiaI time series models. More specifical1y, we concentrate on situations where the factor variances are modeled by a multivariate stochastic volatility structure. We build on previous work by allowing the factor loadings, in the factor mo deI structure, to have a time-varying structure and to capture changes in asset weights over time motivated by applications with multi pIe time series of daily exchange rates. We explore and discuss potential extensions to the models exposed here in the prediction area. This discussion leads to open issues on real time implementation and natural model comparisons.

Simulation -based smoothing and filtering in factor stochastic volatility models : two econometric applications (July-2001)

The past decade has wítenessed a series of (well accepted and defined) financial crises periods in the world economy. Most of these events aI,"e country specific and eventually spreaded out across neighbor countries, with the concept of vicinity extrapolating the geographic maps and entering the contagion maps. Unfortunately, what contagion represents and how to measure it are still unanswered questions. In this article we measure the transmission of shocks by cross-market correlation\ coefficients following Forbes and Rigobon's (2000) notion of shift-contagion,. Our main contribution relies upon the use of traditional factor model techniques combined with stochastic volatility mo deIs to study the dependence among Latin American stock price indexes and the North American indexo More specifically, we concentrate on situations where the factor variances are modeled by a multivariate stochastic volatility structure. From a theoretical perspective, we improve currently available methodology by allowing the factor loadings, in the factor model structure, to have a time-varying structure and to capture changes in the series' weights over time. By doing this, we believe that changes and interventions experienced by those five countries are well accommodated by our models which learns and adapts reasonably fast to those economic and idiosyncratic shocks. We empirically show that the time varying covariance structure can be modeled by one or two common factors and that some sort of contagion is present in most of the series' covariances during periods of economical instability, or crisis. Open issues on real time implementation and natural model comparisons are thoroughly discussed.