Identifying Common Long-Range Dependence in Volume and Volatility Using High-Frequency Data

22 Pages Posted: 31 Aug 2002

See all articles by Roman Liesenfeld

Roman Liesenfeld

University of Cologne, Department of Economics

Date Written: August 2002

Abstract

This paper examines the joint long-run dynamics of trading volume and return volatility in futures contracts on the German stock index DAX using a sample of 5-minute returns and trading volume. Employing robust semiparametric methods of inference on memory parameters, I find that volume and volatility exhibit the same degree of long-memory which is consistent with a mixture-of-distributions (MOD) model in which the latent number of information arrivals follows a long-memory process. However, there is some evidence that volume and volatility are not driven by the same long-memory process suggesting that the MOD model cannot explain the joint long-run dynamics of volatility and volume.

Keywords: Fractional integration, Fractional cointegration, Mixture-of-distributions models, Spectral analysis

JEL Classification: C14, C32

Suggested Citation

Liesenfeld, Roman, Identifying Common Long-Range Dependence in Volume and Volatility Using High-Frequency Data (August 2002). Available at SSRN: https://ssrn.com/abstract=326300 or http://dx.doi.org/10.2139/ssrn.326300

Roman Liesenfeld (Contact Author)

University of Cologne, Department of Economics ( email )

Albertus-Magnus-Platz
D-50931 Köln
Germany

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