Dimitrakopoulos, S orcid.org/0000-0002-0043-180X 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
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved. This is an author produced version of an article published in International Journal of Forecasting. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Conditional heteroscedasticity; In-mean effects; Leverage; Markov chain Monte Carlo; Moving average; Ordinal responses |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Economics Division (LUBS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Feb 2019 13:50 |
Last Modified: | 24 Jul 2021 00:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.ijforecast.2019.02.016 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:142440 |