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In this paper a methodology to compare the performance of different stochastic discount factor (SDF) models is suggested. The starting point is the estimation of several factor models in which the choice of the fundamental factors comes from different procedures. Then, a Monte Carlo simulation is designed in order to simulate a set of gross returns with the objective of mimicking the temporal dependency and the observed covariance across gross returns. Finally, the artificial returns are used to investigate the performance of the competing asset pricing models through the Hansen and Jagannathan (1997) distance and some goodness-of-fit statistics of the pricing error. An empirical application is provided for the U.S. stock market.

Avaliando Modelos de Precificação de Ativos via Abordagem de Fatores Simulados