Nonparametric estimation of time varying correlation coefficient

Ji Eun Choi, Dong Wan Shin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We propose a new time varying correlation coefficient, which is a local correlation measure of a pair of time series. The time varying correlation coefficient is locally estimated using a nonparametric kernel method. Asymptotic normality of the estimated time varying correlation is established, which allows us to construct statistical methods of confidence interval and hypothesis tests. Finite sample validity of the proposed methods are demonstrated by a Monte–Carlo study. The proposed time varying correlation coefficient method is well illustrated by an analysis of five sets of world major stock price index returns.

Original languageEnglish
Pages (from-to)333-353
Number of pages21
JournalJournal of the Korean Statistical Society
Issue number2
StatePublished - Jun 2021

Bibliographical note

Funding Information:
The authors are very thankful of two anonymous referees whose comments improved the paper considerably. This study was supported by a Grant from the National Research Foundation of Korea (2019R1A2C1004679) and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2019R1A6A1A11051177).

Publisher Copyright:
© 2020, Korean Statistical Society.


  • Confidence interval
  • Nonparametric estimation
  • Statistical test
  • Time varying correlation coefficient


Dive into the research topics of 'Nonparametric estimation of time varying correlation coefficient'. Together they form a unique fingerprint.

Cite this