Tree-structured generalized autoregressive conditional heteroscedastic models
Journal
Journal of the Royal Statistical Society, Series B
ISSN
1369-7412
ISSN-Digital
1467-9868
Type
journal article
Date Issued
2001-12-01
Author(s)
Abstract
We propose a new generalized autoregressive conditional heteroscedastic (GARCH) model with tree-structured multiple thresholds for the estimation of volatility in financial time series. The approach relies on the idea of a binary tree where every terminal node parameterizes a (local) GARCH model for a partition cell of the predictor space. The fitting of such trees is constructed within the likelihood framework for non-Gaussian observations: it is very different from the well-known regression tree procedure which is based on residual sums of squares. Our strategy includes the classical GARCH model as a special case and allows us to increase model complexity in a systematic and flexible way. We derive a consistency result and conclude from simulation and real data analysis that the new method has better predictive potential than other approaches.
Language
English
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Royal Statistical Society
Publisher place
London
Volume
63
Number
4
Start page
727
End page
744
Pages
18
Subject(s)
Eprints ID
32631