ABSTRACT
This paper follows the approach of Wang (2003) in order to test the conditional version of Sharpe-Lintner CAPM by adopting Local Maximum Likelihood as its nonparametric methodology. This methodology does not only avoid the misspeficication of betas, risk premiums and the stochastic discount factor but, is also expected to perform better when compared with other more traditional methods such as the constant Nadaraya-Watson kernel estimator due to its superior performance at the sample extremes.
