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

Taylor, N. and Xu, Y. ORCID: https://orcid.org/0000-0001-8275-1585 2016. The logarithmic vector multiplicative error model: an application to high frequency NYSE stock data. Quantitative Finance 17 (7) , pp. 1021-1035. 10.1080/14697688.2016.1260756

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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.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Business (Including Economics)
Subjects:	H Social Sciences > HA Statistics H Social Sciences > HB Economic Theory H Social Sciences > HG Finance
Additional Information:	PDF uploaded in accordance with publisher's policies at http://www.sherpa.ac.uk/romeo/issn/1469-7688/ (accessed 3.3.17).
Publisher:	Taylor & Francis (Routledge)
ISSN:	1469-7688
Date of First Compliant Deposit:	1 March 2017
Date of Acceptance:	9 November 2016
Last Modified:	01 Dec 2024 01:00
URI:	https://orca.cardiff.ac.uk/id/eprint/97387

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