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.

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传染病的不确定性和股票债券的相关性

我们研究了由于传染病引起的不确定性与股票债券相关性之间的非线性因果关系。为此，我们使用高频1分钟数据来计算日常实现的相关性和跳跃量度，然后，使用人工神经网络进行非线性Granger因果关系检验，以研究此类类型的可预测性已实现的股票债券相关性和跳跃性的不确定性。我们的发现表明，由于传染病引起的不确定性对股票债券关系的变化具有重要的预测价值。