Nonparametric estimation of time varying correlation coefficient

Abstract

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. © 2020, Korean Statistical Society.