Granger causality of bivariate stationary curve time series. (4th November 2020)

• Record Type: Journal Article
• Authors: Shang, Han Lin; Ji, Kaiying; Beyaztas, Ufuk
• Is Part Of: Journal of forecasting. Volume 40:Number 4(2021)
• Page Start: 626
• Page End: 635
• Publication Date: 2020-11-04
• Subjects: functional time series -- G‐causality -- Granger causality; Forecasting -- Periodicals; Forecasting -- Mathematical models -- Periodicals; 003.2
• DOI: 10.1002/for.2732
• Languages: English
• ISSNs: 0277-6693

Abstract

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 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.