Optimal Time Interval Selection in Long-Run Correlation Estimation

Pedro Albuquerque

Journal of Quantitative Economics, 2020, vol. 18, issue 1, No 3, 53-79

Abstract: 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 alternative to the customary practice in finance and economics of heuristically or arbitrarily choosing time intervals or lags in correlation studies. A Monte Carlo experiment using parameters derived from Dow Jones returns data confirms that the procedure can be MSE-superior to alternatives such as aggregation over arbitrary time intervals, parametric VAR, and Newey–West covariance matrix estimation with automatic lag selection.

Keywords: Long-run correlation; Bartlett; Lag selection; Alignment; Newey–West; Andrews–Monahan (search for similar items in EconPapers)
JEL-codes: C14 C22 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40953-019-00175-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
Working Paper: Optimal Time Interval Selection in Long-Run Correlation Estimation (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jqecon:v:18:y:2020:i:1:d:10.1007_s40953-019-00175-x

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/40953

DOI: 10.1007/s40953-019-00175-x

Access Statistics for this article

Journal of Quantitative Economics is currently edited by Dilip Nachane and P.G. Babu

More articles in Journal of Quantitative Economics from Springer, The Indian Econometric Society (TIES) Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().