Forecasting time series by long‑memory models for countdata with an application to price jumps

We discuss the estimation and forecast of long-memory models for count data timeseries. We first demonstrate by Monte Carlo simulations that the Whittle estimator isthe most appropriate for recovering the memory degree of a count data time series.In the following, we introduce the possibility of forecasting count data by exploitingthe infinite autoregressive representation of the model. We complete our analysiswith an empirical example in which we verify the predictability of the price jumpnumbers.