Uncertainty Due to Infectious Diseases and Stock-Bond Correlation

We study the non-linear causal relation between uncertainty-due-to-infectious-diseases and stock-bond correlation. To this end, we use high-frequency 1-min data to compute daily realized measures of correlation and jumps, and then, we employ a nonlinear Granger causality test with the use of artificial neural networks so as to investigate the predictability of this type of uncertainty on realized stock-bond correlation and jumps. Our findings reveal that uncertainty-due-to-infectious-diseases has significant predictive value on the changes of the stock-bond relation.

Automated summary

This study examines the non-linear causal relation between uncertainty-due-to-infectious-diseases and stock-bond correlation, finding that this type of uncertainty has significant predictive value on changes in the stock-bond relation. The research used high-frequency data and artificial neural networks to investigate the predictability of uncertainty on realized stock-bond correlation and jumps.