Analysis and compensation of asynchronous stock time series

Sina Jahandari, Donatello Materassi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


The accurate computation of statistical quantities from sampled data is of paramount importance in the analysis of economic and financial time series. The Epps effect is an empirically observed phenomenon where the sample correlation between the logarithmic returns of two stock prices decreases as the sampling frequency of data increases. The full explanation of this phenomenon is currently an open problem and several potential contributing factors are reported in the scientific literature. However, asynchronous sampling times in the stock prices is one of the key components originating the Epps effect. This article investigates in a quantitative way how asynchronous price data contribute to the Epps effect by modeling stock prices as correlated geometric Brownian motions and considering trading times as Poisson point processes. Under these assumptions we show that the Epps effect can be considered as a statistical artifact producing a bias on the sample correlation of the logarithmic returns. We also provide an analytic expression describing this bias. This expression can be used to compensate the bias on the sample correlation in order to obtain an unbiased estimate.

Original languageEnglish (US)
Title of host publication2017 American Control Conference, ACC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509059928
StatePublished - Jun 29 2017
Externally publishedYes
Event2017 American Control Conference, ACC 2017 - Seattle, United States
Duration: May 24 2017May 26 2017

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2017 American Control Conference, ACC 2017
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2017 American Automatic Control Council (AACC).


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