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Granger causality of bivariate stationary curve time series

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  • Han Lin Shang
  • Kaiying Ji
  • Ufuk Beyaztas

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.

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  • Han Lin Shang & Kaiying Ji & Ufuk Beyaztas, 2021. "Granger causality of bivariate stationary curve time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 626-635, July.
    • Handle: RePEc:wly:jforec:v:40:y:2021:i:4:p:626-635
    DOI: 10.1002/for.2732
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    References listed on IDEAS

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    Cited by:

    1. Rituparna Sen & Anandamayee Majumdar & Shubhangi Sikaria, 2022. "Bayesian Testing of Granger Causality in Functional Time Series," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 191-210, September.
    2. Rituparna Sen & Anandamayee Majumdar & Shubhangi Sikaria, 2021. "Bayesian Testing Of Granger Causality In Functional Time Series," Papers 2112.15315, arXiv.org.

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