Granger causality of bivariate stationary curve time series

We study causality between bivariate curve time series using the Granger causality generalized measures of correlation. With this measure, we can investigate which curve time series Granger-causes the other; in turn, it helps determine the predictability of any two curve time series. Illustrated by a climatology example, we find that the sea surface temperature Granger-causes sea-level atmospheric pressure. Motivated by a portfolio management application in finance, we single out those stocks that lead or lag behind Dow Jones industrial averages. Given a close relationship between S&P 500 index and crude oil price, we determine the leading and lagging variables.

Automated summary

By studying causality between bivariate curve time series using Granger causality generalized measures of correlation, we can determine which curve time series influences the other and the predictability of the two series. An example in climatology shows that sea surface temperature Granger-causes sea-level atmospheric pressure, while in finance, we identify stocks that lead or lag behind Dow Jones industrial averages. The close relationship between the S&P 500 index and crude oil price helps us determine leading and lagging variables.