Forecasting transaction counts with integer-valued GARCH models

Abdelhakim Aknouche, Bader Almohaimeed and Stefanos Dimitrakopoulos

MPRA Paper from University Library of Munich, Germany

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 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; INGARCH models; MCMC; Forecasting comparison (search for similar items in EconPapers)
JEL-codes: C1 C11 C15 C18 C4 C58 (search for similar items in EconPapers)
Date: 2020-07-11, Revised 2020-07-11
New Economics Papers: this item is included in nep-ets, nep-for and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:101779

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