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One of the greatest challenges of Finance researchers is to study the trade-o¤ between risk and return of market assets. Common sense would lead us to a¢ rm that as higher an assets risk, the greater its associated return. Markowitz (1959) has developed the research basis that links risk and return, achieving the CAPM model (Capital Asset Pricing Model). There are practically two existing methods in order to validate the non-conditional CAPM model: The Maximum Likelihood Method (ML), which supposes that assets (or portfolios) returns are IID and could be represented by a multivariate normal (gaussian) distribution; and the Generalized Moments Method (GMM), which do not supposes that the assets returns (or portifolios returns) are IID and, furthermore, that might have any specic distribution. This dissertation will be pioneer in testing the validity of the CAPM zero-beta model by GMM. First, we economically derive the non-conditional CAPMmodel, either to the Sharpe-Lintner version as to the zero-beta version, supposing that the return pairs (Ri; Rm) have a bivariate elliptical distribution and that the investorsutility function be increaesing and concave. The GMM method have been choosen in order to test non-conditional CAPM model in Brazilian capital markets, because the daily log-returns series of the analised shares did not showed itselves as normal and IID. We have realised that the SL CAPM model, either in terms of SELIC rate as of CDI rate (risk-free assets), can not be rejected at 5% level for the period from 2/1/00 until 31/12/04. For the periods from 2/1/95 until 31/12/99 and from 2/1/95 until 31/12/04, the given model was rejected at the 5% level. This way, for the SL CAPM model, either in terms of SELIC rate as of CDI rate, the BOVESPA index have behaved as an e¢ cient portfolio only on the period from 2/1/00 until 31/12/04. For the zero-beta CAPM model, it can be veried that we cannot reject it at the 5% level in none of the three periods analysed above. Sumário Lista de Tabelas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2 Lista de Figuras . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3 1 Introdução . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5 1.1 Justicativa do tema . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2 Questão de pesquisa. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.3 Objetivos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.4 Metodologia da pesquisa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2 Fundamentos teóricos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1 Conjunto eciente . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13 2.1.1 Propriedades matemáticas do conjunto eciente . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17 2.2 Derivação econômica do modelo CAPM não-condicional . . . . . . . . . . . . . . . . . . . . . . . . . .24 2.2.1 Pré-requisitos conceituais . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28 2.3 CAPM e Informação Contábil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37 3 Métodos econométricos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.1 Abordagem por máxima verossimilhança (MV) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41 3.1.1 Versão Sharpe-Lintner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41 3.1.2 Versão zero-beta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .46 3.2 Abordagem pelo método dos momentos generalizados (GMM) . . . . . . . . . . . . . . . . . . . .50 3.2.1 Versão Sharpe-Lintner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .53 3.2.2 Versão zero-beta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .56 3.3 Escolha do método econométrico . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .57 3.4 Vericação da hipótese de IID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .58 3.5 Vericação da normalidade multivariada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .60 4 Resultados Obtidos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.1 Conclusão . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .72 A Programas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Referências Bibliográcas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Avaliação empírica do modelo CAPM no mercado de capitais brasileiro via método dos momentos generalizados