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

1 Citation (Scopus)
2 Downloads (Pure)


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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