| Full text | |
| Author(s): |
Total Authors: 3
|
| Affiliation: | [1] Univ Estadual Campinas, Dept Stat, UNICAMP, Campinas, SP - Brazil
[2] Pontificia Univ Catolica Peru, Fac Ciencias & Ingn, San Miguel Lima - Peru
[3] Univ Sao Paulo, Dept Appl Math & Stat, BR-03178200 Sao Paulo - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION; v. 46, n. 10, p. 7942-7956, 2017. |
| Web of Science Citations: | 0 |
| Abstract | |
In this article, we perform Bayesian estimation of stochastic volatility models with heavy tail distributions using Metropolis adjusted Langevin (MALA) and Riemman manifold Langevin (MMALA) methods. We provide analytical expressions for the application of these methods assess the performance of these methodologies in simulated data, and illustrate their use on two financial time series datasets. (AU) | |
| FAPESP's process: | 15/00627-9 - Bayesian Inference, Metropolis-Langevin and Hamiltonian in Riemann manifolds. |
| Grantee: | Ricardo Sandes Ehlers |
| Support Opportunities: | Scholarships abroad - Research |
| FAPESP's process: | 13/00506-1 - Time series, wavelets and functional data analysis |
| Grantee: | Pedro Alberto Morettin |
| Support Opportunities: | Research Projects - Thematic Grants |