Bivariate and higher-order terms in models of international equity returns

Abstract

Nonsynchronous measurement induces significant higher-order auto and serial cross correlations in observed bivariate returns and squared returns to international equity indices. In order to investigate the statistical and economic significance of bivariate and higher-order terms in conditional models of international equity returns, we fit a VMA(2)–EGARCH(2,2) model with normal errors and a constant conditional correlation using MSCI index pairs for Japan, the UK and the USA. First-order univariate and bivariate conditional mean and volatility terms are statistically significant in each series. Second-order own- and cross-volatility terms also are significant, although second-order conditional mean terms are not. We investigate the economic significance of bivariate and second-order terms by comparing the return and volatility predictions of various models using out-of-sample regressions of returns or squared returns on conditional means or volatilities. Bivariate terms significantly improve return predic...