Forecasting transaction counts with integer-valued GARCH models
Aknouche Abdelhakim (),
Almohaimeed Bader S. () and
Dimitrakopoulos Stefanos ()
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Almohaimeed Bader S.: Department of Mathematics, College of Science, Qassim University, P.O. Box 707, Buraydah, 51431, Saudi Arabia
Dimitrakopoulos Stefanos: Economics Division, Leeds University Business School, University of Leeds, LS2 9JT, Leeds, UK
Studies in Nonlinear Dynamics & Econometrics, 2022, vol. 26, issue 4, 529-539
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.
Keywords: count time series; forecasting; INGARCH models; MCMC (search for similar items in EconPapers)
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