The logarithmic vector multiplicative error model: an application to high frequency NYSE stock data

Nick Taylor*, Yongdeng Xu

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

4 Citations (Scopus)
262 Downloads (Pure)

Abstract

We develop a general form logarithmic vector multiplicative error model (log-vMEM). The log-vMEM improves on existing models in two ways. First, it is a more general form model as it allows the error terms to be cross-dependent and relaxes weak exogeneity restrictions. Second, the log-vMEM specification guarantees that the conditional means are non-negative without any restrictions imposed on the parameters. We further propose a multivariate lognormal distribution and a joint maximum likelihood estimation strategy. The model is applied to high frequency data associated with a number of NYSE-listed stocks. The results reveal empirical support for full interdependence of trading duration, volume and volatility, with the log-vMEM providing a better fit to the data than a competing model. Moreover, we find that unexpected duration and volume dominate observed duration and volume in terms of information content, and that volatility and volatility shocks affect duration in different directions. These results are interpreted with reference to extant microstructure theory.

Original languageEnglish
Number of pages15
JournalQuantitative Finance
Early online date21 Dec 2016
DOIs
Publication statusE-pub ahead of print - 21 Dec 2016

Keywords

  • vMEM
  • ACD
  • intraday trading process
  • duration
  • volume
  • volatility

Fingerprint

Dive into the research topics of 'The logarithmic vector multiplicative error model: an application to high frequency NYSE stock data'. Together they form a unique fingerprint.

Cite this