Granger causality of bivariate stationary curve time series
Methodology
2020-10-21 v2 Applications
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.
Keywords
Cite
@article{arxiv.2010.05320,
title = {Granger causality of bivariate stationary curve time series},
author = {Han Lin Shang and Kaiying Ji and Ufuk Beyaztas},
journal= {arXiv preprint arXiv:2010.05320},
year = {2020}
}
Comments
17 pages, 3 figures, to appear at the Journal of Forecasting