Please use this identifier to cite or link to this item: https://repositorio.insper.edu.br/handle/11224/4098
Type: Artigo Científico
Title: Bayesian semiparametric Markov switching stochastic volatility model
Author: Virbickaité, Audrone
Lopes, Hedibert Freitas
Publication Date: 2019
Abstract: This paper proposes a novel Bayesian semiparametric stochastic volatility model with Markov switching regimes for modeling the dynamics of the financial returns. The distribution of the error term of the returns is modeled as an infinite mixture of Normals; meanwhile, the intercept of the volatility equation is allowed to switch between two regimes. The proposed model is estimated using a novel sequential Monte Carlo method called particle learning that is especially well suited for state-space models. The model is tested on simulated data and, using real financial times series, compared to a model without the Markov switching regimes. The results show that including a Markov switching specification provides higher predictive power for the entire distribution, as well as in the tails of the distribution. Finally, the estimate of the persistence parameter decreases significantly, a finding consistent with previous empirical studies.
Keywords (english terms): Bayes factor
Dirichlet process mixture
Particle learning
Sequential Monte Carlo
Language: Inglês
CNPq Area: Ciências Sociais Aplicadas
URI: https://onlinelibrary.wiley.com/doi/10.1002/asmb.2434
Copyright: O INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR.
Appears in Collections:Coleção de Artigos Científicos

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