Estimating the Correlation of Non-Contemporaneous Time-Series

42 Pages Posted: 15 Jun 2007 Last revised: 8 Sep 2008

See all articles by Thomas Coleman

Thomas Coleman

University of Chicago - Harris School of Public Policy; Close Mountain Advisors LLC

Date Written: December 13, 2007

Abstract

Daily financial time-series are often observed with different closing times: for example the FTSE stock index closes 11am NY time while the S&P500 stock index closes 4pm NY time. The non-contemporaneous observations mean the na┬┐ve correlation estimator is biased. This paper reviews the problem, discusses some simple estimators previously used in the literature, develops a maximum likelihood estimator, and compares estimators via simulations. We find two important results. First, a pseudo-ML estimator performs best, while simple method-of-moment estimators perform well in certain cases. Second, the standard error of the correlation estimator can be surprisingly large relative to the contemporaneous observations case.

Keywords: Covariance, Correlation, Asynchronous Trading, Non-synchronous Trading, Closing-Time Problem

JEL Classification: C13, G10

Suggested Citation

Coleman, Thomas, Estimating the Correlation of Non-Contemporaneous Time-Series (December 13, 2007). Available at SSRN: https://ssrn.com/abstract=987119 or http://dx.doi.org/10.2139/ssrn.987119

Thomas Coleman (Contact Author)

University of Chicago - Harris School of Public Policy ( email )

1155 East 60th Street
Chicago, IL 60637
United States

Close Mountain Advisors LLC ( email )

19 Davenport Ave.
Unit B
Greenwich, CT 06830
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
329
Abstract Views
1,657
rank
99,205
PlumX Metrics