Stationary-Increment Variance-Gamma and "t" Models: Simulation and Parameter Estimation

We detail a method of simulating data from long range dependent processes with variance-gamma or "t" distributed increments, test various estimation procedures [method of moments (MOM), product-density maximum likelihood (PMLE), non-standard minimumχ-super-2and empirical characteristic function estimation] on the data, and assess the performance of each. The investigation is motivated by the apparent poor performance of the MOM technique using real data (Tjetjep & Seneta, 2006); and the need to assess the performance of PMLE for our dependent data models. In the simulations considered the product-density method performs favourably. Copyright (c) 2008 The Authors. Journal compilation (c) 2008 International Statistical Institute.