Beyond Correlation: Extreme Co-Movements between Financial Assets

Abstract

This paper investigates the potential for extreme co-movements between financial assets by directly testing the underlying dependence structure. In particular, a t-dependence structure, derived from the Student t-distribution, is used as a proxy to test for this extremal behavior. Tests in three different markets (equities, currencies, and commodities) indicate that extreme co-movements are statistically significant. Moreover, the "correlation-based" Gaussian dependence structure, underlying the multivariate Normal distribution, is rejected with negligible error probability when tested against the t-dependence alternative. The economic significance of these results is illustrated via three examples: co-movements across the G5 equity markets; portfolio value-at-risk calculations; and, pricing credit derivatives.

This paper investigates the potential for extreme co-movements between financial assets by directly testing the underlying dependence structure. In particular, a t-dependence structure, derived from the Student t-distribution, is used as a proxy to test for this extremal behavior. Using likelihood ratio-based methods, we show that the presence of extreme co-movements is statistically significant in three asset markets (equities, currencies, and commodities), as well as across international (G5) equity markets. In addition, this likelihood ratio test indicates that the "correlation-based" Gaussian dependence structure is not supported on the basis of observed asset co-movements. The economic significance and consequences of these results are illustrated via several examples.

Keywords: asset returns, extreme co-movements, copulas, dependence modeling, hypothesis testing, international markets, pseudo-likelihood, portfolio models, risk management

JEL Classification: C12, C15, C52, G11