Granger causality of bivariate stationary curve time series

TL;DR

This paper explores causality between bivariate curve time series using generalized correlation measures, with applications in climatology and finance to identify leading and lagging variables.

Contribution

It introduces a method to assess Granger causality for bivariate curve time series and demonstrates its effectiveness through real-world climatology and finance examples.

Findings

Sea surface temperature Granger-causes sea-level pressure

Identifies stocks leading or lagging Dow-Jones averages

Determines leading and lagging variables between S&P 500 and crude oil

Abstract

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 the 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.