On the Robustness of Symmetry Tests for Stock Returns

In this paper, by using a generalized asymmetry measure with the heteroskedasticity autocorrelation consistent estimation method and a long-run variance eliminating method, we propose two generalized symmetry tests in the presence of unknown distributions and serial dependence. The proposed tests encompass existing skewness tests, and generate new symmetry tests that are robust to both the heavy-tails and the serial dependence of stock returns. We also utilize the concept of an augmented distribution to establish an asymmetric distribution family that encompasses Pearson's type-IV distribution, and we use this distribution family and the score test principle to discuss the choice of asymmetry measures for testing symmetry. In this study, we also compare our tests with existing tests using a Monte Carlo simulation and an empirical example, and show that the robust tests outperform existing tests for checking the symmetry of stock returns.