Previsão do câmbio real-dolar sobre um arcabouço de apreçamento de ativos

• Autor(a) principal: Beviláqua, Giovanni Silva
• Data de Publicação: 2011
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da Universidade Federal do Ceará (UFC)
• Texto Completo: http://www.repositorio.ufc.br/handle/riufc/5796
• topic: Mercado Cambial; Moedas; Real Brasileiro; Dolar Americano

Given the wide range of macroeconomic, financial and econometric frameworks commonly used to accommodate uncomfortable empirical evidence associated with the Forex market, this article aims to model and predict the monthly variation in American Dollar-Brazilian Real exchange rate, from January 2000 to December 2009, based on asset pricing theory. Wang (2008) and Engel and West (2005) are closer to ours, in terms of fundamentals of finance, while methodologically, we are close to Chong, Chung and Ahmad (2002) and da Costa et al. (2010). Our work is relevant to the empirical literature, since the prediction results are better than the random walk approach ones. The prediction error is about 5% and 14% for the exchange rate variation and in level, respectively. In 57.5% of the changes, our model predicts the correct change direction. The main contribution based on this framework, already used to understand the Forward Premium Puzzle for advanced economies, consists in the derivation and the implications of a system of linear relationships characterized by a Bivariate Generalized Autoregressive Conditional Heteroskedasticity-in-Mean (GARCH-M), useful empirically, once we have extracted a time series for a Stochastic Discount Factor (SDF) able to price the covered and the uncovered trading with U.S. Government bonds. The results suggest to the theoretical literature that, at least for monthly frequency, one should not omit the temporal variation of conditional moments of the second order. The hypothesis about the lognormal distribution of discounted returns and a parsimonious specification for conditional Heteroskedastic models can influence the predictive power of SDF, as well as the effects of the inclusion of risk premium.