| Full text | |
| Author(s): |
Total Authors: 2
|
| Affiliation: | [1] Univ Brasilia, CIC EST, Darcy Ribeiro Campus, Brasilia, DF - Brazil
[2] Univ Sao Paulo, IME, Matao St, Sao Paulo - Brazil
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | ECONOMETRICS AND STATISTICS; v. 15, p. 67-83, JUL 2020. |
| Web of Science Citations: | 3 |
| Abstract | |
The class of Randomized Generalized Autoregressive Conditional Heteroskedastic (R-GARCH) models represents a generalization of the GARCH models, adding a random term to the volatility with the purpose to better accommodate the heaviness of the tails expected for returns in the financial field. In fact, it is assumed that this term has stable distribution. Allowing both, returns and volatility, to have stable distribution, a new class of models to describe volatility arises: Stable Randomized Generalized Autoregressive Conditional Heteroskedastic Models (SR-GARCH). The indirect inference method is proposed to estimate the SR-GARCH parameters, theoretical results concerning dependence structure are obtained. Simulations and an empirical application are presented. (C) 2018 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 13/00506-1 - Time series, wavelets and functional data analysis |
| Grantee: | Pedro Alberto Morettin |
| Support Opportunities: | Research Projects - Thematic Grants |