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Optimal Time Interval Selection in Long-Run Correlation Estimation

Posted: 21 Apr 2005 Last revised: 5 Feb 2017

Pedro H. Albuquerque

KEDGE Business School; Aix-Marseille University - Aix-Marseille School of Economics

Date Written: October 1, 2006

Abstract

This paper presents an asymptotically optimal time interval selection criterion for the long-run correlation block estimator (Bartlett kernel estimator) based on the Newey-West and Andrews-Monahan approaches. An alignment criterion that enhances finite-sample performance is also proposed. The procedure offers an optimal yet unobtrusive alternative to the common practice in finance and economics of arbitrarily choosing time intervals or lags in correlation studies. A Monte Carlo experiment using parameters derived from Dow Jones returns data confirms that the procedures are MSE-superior to typical alternatives such as aggregation over arbitrary time intervals, parametric VAR estimation, and Newey-West covariance matrix estimation with automatic lag selection.

Keywords: Long-Run Correlation, Bartlett, Lag Selection, Time Interval, Alignment, Newey-West, Andrews-Monahan

JEL Classification: C14

Suggested Citation

Albuquerque, Pedro H., Optimal Time Interval Selection in Long-Run Correlation Estimation (October 1, 2006). Available at SSRN: https://ssrn.com/abstract=702650 or http://dx.doi.org/10.2139/ssrn.702650

Pedro H. Albuquerque (Contact Author)

KEDGE Business School ( email )

Domaine de Luminy - BP 921
BP 921
Marseille, PACA 13288
France

HOME PAGE: http://www.kedgebs.com/en

Aix-Marseille University - Aix-Marseille School of Economics

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Marseille, 13236
France

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