A distribution-free test for symmetry with an application to S&P index returns

Abstract

We propose a distribution-free test of symmetry. Monte Carlo results show the new test usually outperforms the nonnormality robust version of the Jarque–Bera test. Empirical results indicate that the tail of the distribution is too heavy to apply the latter test, while the former is always valid.