| Texto completo | |
| Autor(es): |
Abbara, Omar
;
Zevallos, Mauricio
Número total de Autores: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | STUDIES IN NONLINEAR DYNAMICS AND ECONOMETRICS; v. N/A, p. 24-pg., 2022-03-25. |
| Resumo | |
Stochastic Volatility (SV) models are an alternative to GARCH models for estimating volatility and several empirical studies have indicated that volatility exhibits long-memory behavior. The main objective of this work is to propose a new method to estimate a univariate long-memory stochastic volatility (LMSV) model. For this purpose we formulate the LMSV model in a state-space representation with non-Gaussian perturbations in the observation equation, and the estimation of parameters is performed by maximizing the likelihood written in terms derived from a Kalman filter algorithm. We also present a procedure to calculate volatility and Value-at-Risks forecasts. The proposal is evaluated by means of Monte Carlo experiments and applied to real-life time series, where an illustration of market risk calculation is presented. (AU) | |
| Processo FAPESP: | 18/04654-9 - Séries temporais, ondaletas e dados de alta dimensão |
| Beneficiário: | Pedro Alberto Morettin |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |