Asymmetric Correlation in Predicting Portfolio Returns

Rigorous statistical tests have been designed to detect the existence of asymmetric correlations. However, these tests can hardly further facilitate future investment or risk management because asymmetric correlations are time-varying and difficult to predict. In this paper, we construct a unified state-space model, which not only measures in-sample asymmetric correlations, but also exploit out-of-sample asymmetric correlations in the context of predicting portfolio returns. First, we regard time-varying correlation between market returns and portfolio returns as a state variable and model it as an AR(1) process. Then, we measure future asymmetric correlations based on correlation coefficients between two unpredictable components in market returns and correlation, respectively. Third, we clarify the intuition, calculate asymmetric correlations for two portfolio sets and estimate the economic value of applying our model in asset allocation. Finally, we try to search for potential variables that can explain future asymmetric correlations. The results show that market-wide liquidity, variance, earning price ratio, and investor sentiment can partially explain the asymmetry correlation phenomenon.