Asymmetric Correlation is an empirical observation that correlations in stock market price trends are higher during market downtrends than their correlations in uptrends. Asymmetric Correlation observes that stock prices tend to fall together with high serial correlation more than prices rise in up trending markets. For example, in Asymmetric Correlations of Equity Portfolios. Andrew Ang Columbia University and NBER Joseph Chen Stanford University find:
Correlations between U.S. stocks and the aggregate U.S. 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 asymmetries, such as 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 hypothesis of multivariate normal distributions at daily, weekly, and monthly frequencies. However, several empirical models with greater flexibility, particularly regime-switching models, perform better at capturing correlation asymmetries.
Thinking of Asymmetric Correlation as an investment objective:
Asymmetric Correlation is a low correlation in declining markets and a high correlation in up trending markets. Asymmetric Correlation is an empirical observation that correlations between security returns are significantly higher during market downturns compared to upturns.