Aknouche, A, Almohaimeed, BS and Dimitrakopoulos, S orcid.org/0000-0002-0043-180X (2022) Forecasting transaction counts with integer-valued GARCH models. Studies in Nonlinear Dynamics and Econometrics, 26 (4). pp. 529-539. ISSN 1081-1826
Abstract
Using numerous transaction data on the number of stock trades, we conduct a forecasting exercise with INGARCH models, governed by various conditional distributions; the Poisson, the linear and quadratic negative binomial, the double Poisson and the generalized Poisson. The model parameters are estimated with efficient Markov Chain Monte Carlo methods, while forecast evaluation is done by calculating point and density forecasts.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Walter de Gruyter GmbH. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | count time series; forecasting; INGARCH models; MCMC |
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: | 01 Sep 2021 15:21 |
Last Modified: | 29 Oct 2024 13:55 |
Status: | Published |
Publisher: | De Gruyter |
Identification Number: | 10.1515/snde-2020-0095 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177614 |