Analysis of Dependencies in Low Frequency Financial Data Sets

Posted: 15 Apr 2004 Last revised: 14 Dec 2015

See all articles by David Ardia

David Ardia

HEC Montreal - Department of Decision Sciences

Date Written: January 11, 2003

Abstract

This empirical study proposes a dependency analysis of monthly financial time series. We use the overlapping technique and non-parametric correlation in order to increase both accuracy and consistency. Copulas are used to test extreme co-movements between financial securities. Our results indicate that even in a low-frequency framework, the common practice of assuming independence over time should be taken with caution due to the presence of GARCH effects. In addition, extreme co-movements are observed across securities, especially for interest rates.

Keywords: Dependencies, low-frequency, monthly, copula, GARCH

JEL Classification: C10, C13, C14, C50, G22

Suggested Citation

Ardia, David, Analysis of Dependencies in Low Frequency Financial Data Sets (January 11, 2003). Available at SSRN: https://ssrn.com/abstract=531022 or http://dx.doi.org/10.2139/ssrn.531022

David Ardia (Contact Author)

HEC Montreal - Department of Decision Sciences ( email )

3000 Côte-Sainte-Catherine Road
Montreal, QC H2S1L4
Canada

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