Explaining the coherency of national stock indices with macroeconomic variables: Time-series correlation and Cross-sectional correlation approaches

Abstract: The phenomenon of increasing correlation between asset returns in economic downturns will be investigated with two different approaches and tried to be explained by different macroeconomic variables. The first approach, namely the classic method of measuring correlation with time series is contrasted with an extended method of cross-sectional correlation measurement proposed by Solnik (2000). The method was applied to sub-indices of the German stock market. Adjacent to the sub-index returns several macroeconomic variables were used in OLS regressions as regressors. In order to test for time variability of the variables’ explanatory power subsamples were built. The models were tested with monthly data starting in January 1991 and ending in December 2009. Furthermore, several econometric tests were accomplished to evaluate the econometric quality of the different approaches. Several results were found: The classic time series approach outperforms the cross-sectional approach in terms of econometric quality. Moreover, the former backed the theory of increasing correlations in down-states whereas the latter could not. Nevertheless, the findings of the regressions were very similar: No variable is consistent enough to be used as predictive variable, but in general the amount of credits given to enterprises and the number of unemployed people help to explain return correlation movements over time. However, all regressors suffer from time variability. Splitting the results to the different sub-indices and its appendent correlations gives further sector specific results.