Economics at your fingertips  

An investigation on the relationship between return and trading volume: asymmetric V-type or asymmetric increasing-type pattern

Kuang-Liang Chang and Shih-Ti Yu

Quantitative Finance, 2017, vol. 17, issue 8, 1223-1241

Abstract: This paper develops a new mechanism that takes into account the fast change in behaviours of futures returns and trading volumes in order to model the time-varying and quantile-varying dependence between return and volume for energy-related futures products traded on TOCOM, NYMEX and ICE Futures Europe. A logistic function with the product of one-step-ahead expectations of return and volume as a transition variable is used to depict the time-varying weight of a mixture copula. This paper then employs a mixture copula of a Gumbel copula and a rotated Gumbel copula to detect the asymmetric V-type pattern and uses a mixture copula of a Gumbel copula and a survival Gumbel copula to measure the asymmetric increasing-type pattern. Empirical results demonstrate that the asymmetric V-type pattern is a more appropriate specification to characterize the return–volume nexus than the asymmetric increasing-type pattern, irrespective of the types of energy-related futures products and futures exchanges. The time-varying dependence has greater dependence in the lower–upper corner of the joint distribution than in the upper–upper corner of the joint distribution, implying that market participants believe that market reversals are more likely during periods of price declines than in periods of price increases. Moreover, this paper shows the inappropriateness of the two-step estimation method that has been widely used in the existing literature.

Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link) (text/html)
Access to full text is restricted to subscribers.

Related works:
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:

Ordering information: This journal article can be ordered from

DOI: 10.1080/14697688.2016.1264619

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

Page updated 2020-09-04
Handle: RePEc:taf:quantf:v:17:y:2017:i:8:p:1223-1241