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Long memory and tail dependence in trading volume and volatility

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  • Rossi, Eduardo
  • Santucci de Magistris, Paolo

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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Empirical Finance.

Volume (Year): 22 (2013)
Issue (Month): C ()
Pages: 94-112

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Handle: RePEc:eee:empfin:v:22:y:2013:i:c:p:94-112

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Web page: http://www.elsevier.com/locate/jempfin

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Keywords: Realized volatility; Trading volume; FIVAR; Tail dependence; Copula modeling;

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Cited by:
  1. Piotr Gurgul & Robert Syrek, 2013. "Testing of Dependencies between Stock Returns and Trading Volume by High Frequency Data," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 11(4 (Winter), pages 353-373.

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