Long memory and tail dependence in trading volume and volatility

Eduardo Rossi and Paolo Santucci de Magistris

Journal of Empirical Finance, 2013, vol. 22, issue C, 94-112

Abstract: We investigate the relationship between volatility, measured by realized volatility, and trading volume for 25 NYSE stocks. We show that volume and volatility are long memory but not fractionally cointegrated in most cases. We also find right tail dependence in the volatility and volume innovations. Tail dependence is informative on the behavior of the volatility and volume when large surprising news impact the market. We estimate a fractionally integrated VAR with shock distributions modeled with a mixture of copula functions. The model is able to capture the main characteristics of the series, say long memory, marginal non-normality and tail dependence. A simulation and forecasting exercise highlight the importance of modeling both long memory and tail dependence to capture extreme events.

Keywords: Realized volatility; Trading volume; FIVAR; Tail dependence; Copula modeling (search for similar items in EconPapers)
JEL-codes: C32 G12 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (26)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0927539813000236
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Long Memory and Tail dependence in Trading Volume and Volatility (2009)
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:eee:empfin:v:22:y:2013:i:c:p:94-112

DOI: 10.1016/j.jempfin.2013.03.004

Access Statistics for this article

Journal of Empirical Finance is currently edited by R. T. Baillie, F. C. Palm, Th. J. Vermaelen and C. C. P. Wolff

More articles in Journal of Empirical Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().