Granger causality of bivariate stationary curve time series

Han Lin Shang*, Kaiying Ji, Ufuk Beyaztas

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)626-635
Number of pages10
JournalJournal of Forecasting
Issue number4
Early online date15 Oct 2020
Publication statusPublished - Jul 2021


  • G-causality
  • Granger causality
  • functional time series


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