Multivariate Tests of Mean–Variance Efficiency With Possibly Non-Gaussian Errors
提出在市场组合均值-方差有效性检验中允许非高斯误差的精确检验方法，基于蒙特卡洛技术实现，实证发现高斯假设被拒绝但允许非正态后拒绝次数减少。
We develop exact mean–variance efficiency tests of the market portfolio in the context of (conditional and unconditional) capital asset pricing models (CAPM), allowing for a wide class of possibly non-Gaussian error distributions. The proposed procedures are applicable in a general multivariate linear regression framework, and exactness is achieved through Monte Carlo test techniques. We also perform exact multivariate diagnostic checks. Empirical results show that the Gaussian assumption is rejected, temporal instabilities are apparent, and mean–variance efficiency is rejected over several subperiods, but finite-sample methods that allow for nonnormality and conditioning information substantially reduce the number of rejections.