Retesting the estimation of a utility-based asset pricing model using normal mixture GARCH (1, 1)

Abstract

The main purpose of this paper is to derive the process of estimating dynamic RRA with the maximum likelihood and a Bayesian method having a weakly informative prior density while assuming that the log excess returns on the market are distributed as normal mixture, GARCH(1,1), Mixture GARCH (1, 1). Simulation analysis has been used to compare MLE and Bayesian estimates. Empirical results using GARCH model are presented using market rates of returns and risk-free rates over the period 1941 to 2010.