Abstract
Correlations between US stocks and the aggregate US market are much greater for downside moves, especially for extreme downside moves, than for upside moves. We develop a new statistic for measuring, comparing and testing asymmetries in conditional correlations. Conditional on the downside, correlations in the data differ from the conditional correlations implied by a normal distribution by 11.6%. We find that conditional asymmetric correlations are fundamentally different from other measures of asymmetry like skewness and co-skewness. We find that small stocks, value stocks and past loser stocks have more asymmetric movements. Controlling for size, we find that stocks with lower betas exhibit greater correlation asymmetries and we find no relationship between leverage and correlation asymmetries. Correlation asymmetries in the data reject the null of multivariate normal distributions at daily, weekly and monthly frequencies. However, several empirical models with greater flexibility, particularly regime-switching models, perform much better at capturing correlation asymmetries.
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