Autoregressive conditional duration models for high frequency financial data: an empirical study on mid cap exchange traded funds
Studies in Economics and Finance
ISSN: 1086-7376
Article publication date: 20 October 2021
Issue publication date: 14 January 2022
Abstract
Purpose
The purpose of this study is to identify appropriate autoregressive conditional duration (ACD) models that can capture the dynamics of tick-by-tick mid-cap exchange traded funds (ETFs) for the period July 2017 to December 2017 and accurately predict future trade duration values. The forecasted durations are then used to demonstrate the practical usefulness of the ACD models in quantifying an intraday time-based risk measure.
Design/methodology/approach
Through six functional forms and six error distributions, 36 ACD models are estimated for eight mid-cap ETFs. The Akaike information criterion and Bayesian information criterion and the Ljung-Box test are used to evaluate goodness-of-fit while root mean square error and the Superior predictive ability test are applied to assess forecast accuracy.
Findings
The Box-Cox ACD (BACD), augmented Box-Cox ACD (ABACD) and additive and multiplicative ACD (AMACD) extensions are among the best fits. The results obtained prove that higher degrees of flexibility do not necessarily enhance goodness of fit and forecast accuracy does not always depend on model adequacy. BACD and AMACD models based on the generalised-F distribution generate the best forecasts, irrespective of the trading frequencies of the ETFs.
Originality/value
To the best of the authors’ knowledge, this is the first study that analyses the empirical performance of ACD models for high-frequency ETF data. Additionally, in comparison to previous works, a wider range of ACD models is considered on a reasonably longer sample period. The paper will be of interest to researchers in the area of market microstructure and to practitioners engaged in high-frequency trading.
Keywords
Acknowledgements
The authors are thankful to the Editor and two anonymous reviewers for their valuable comments and suggestions on the previous version of this paper. The authors declare no conflicts of interest.
Citation
Nunkoo, H.B.S., Gonpot, P.N., Sookia, N.-U. and Ramanathan, T.V. (2022), "Autoregressive conditional duration models for high frequency financial data: an empirical study on mid cap exchange traded funds", Studies in Economics and Finance, Vol. 39 No. 1, pp. 150-173. https://doi.org/10.1108/SEF-04-2021-0146
Publisher
:Emerald Publishing Limited
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