Forecasting transaction counts with integer-valued GARCH models

Abstract Using numerous transaction data on the number of stock trades, we conduct a forecasting exercise with INGARCH models, governed by various conditional distributions; Poisson, linear and quadratic negative binomial, double Poisson generalized Poisson. The model parameters are estimated efficient Markov Chain Monte Carlo methods, while forecast evaluation is done calculating point density forecasts.