Faculty of Business Administration
Visiting Scholar Seminar
LADE-based Inferences for Autoregressive Models with Heavy-tailed G-GARCH(1, 1) Noise
Prof. Xingfa Zhang
School of Economics and Statistics
Date: 16 December 2020 (Wednesday)
This paper explores the least absolute deviation (LAD) estimator of the autoregressive
model with heavy-tailed G-GARCH(1, 1) noise. When the tail index α ∈ (1, 2], it is shown that the LAD estimator asymptotically converges to a linear function of a series of α-stable random vectors with a rate of convergence n1−1/α. The result is significantly different from that of the corresponding least square estimator that is not consistent, and partially solves the problem on the asymptoticity of the LAD estimator when the tail index is less than 2. A simulation study is carried out to assess the performance of the LAD estimator and a real example is given to illustrate this approach.
Dr. Xingfa Zhang is now an Associate Professor at School of Economics and Statistics at GZU. His research interest is financial time series analysis. Dr Zhang has published more than 20 papers. His papers appear on a wide variety of journals, including Journal of Econometrics, Statistics and its Interface, Statistics and Probability Letter, Communications in Statistics: Theory and Methods, and Science China Mathematics.