Ordinal-response GARCH models for transaction data:A forecasting exercise

Dimitrakopoulos, S. and Tsionas, M. (2019) Ordinal-response GARCH models for transaction data:A forecasting exercise. International Journal of Forecasting, 35 (4). pp. 1273-1287. ISSN 0169-2070

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Abstract

We use numerous high-frequency transaction data sets to evaluate the forecasting performances of several dynamic ordinal-response time series models with generalized autoregressive conditional heteroscedasticity (GARCH). The specifications account for three components: leverage effects, in-mean effects and moving average error terms. We estimate the model parameters by developing Markov chain Monte Carlo algorithms. Our empirical analysis shows that the proposed ordinal-response GARCH models achieve better point and density forecasts than standard benchmarks.

Item Type:
Journal Article
Journal or Publication Title:
International Journal of Forecasting
Additional Information:
This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, 35, 4, 2019 DOI: 10.1016/j.ijforecast.2019.02.016
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1400/1403
Subjects:
ID Code:
136023
Deposited By:
Deposited On:
12 Aug 2019 13:15
Refereed?:
Yes
Published?:
Published
Last Modified:
30 Mar 2020 10:34