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Estimation and forecasting of long memory stochastic volatility models

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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