Riemann manifold Langevin methods on stochastic volatility estimation (2017)
- Authors:
- Autor USP: EHLERS, RICARDO SANDES - ICMC
- Unidade: ICMC
- DOI: 10.1080/03610918.2016.1255972
- Subjects: PROCESSOS ESTOCÁSTICOS; INFERÊNCIA BAYESIANA; INFERÊNCIA ESTATÍSTICA
- Keywords: Bayesian analysis; Langevin methods; Markov chain Monte Carlo; Metropolis–Hastings; Value at Risk
- Language: Inglês
- Imprenta:
- Publisher place: Philadelphia
- Date published: 2017
- Source:
- Título do periódico: Communications in Statistics - Simulation and Computation
- ISSN: 0361-0918
- Volume/Número/Paginação/Ano: v. 46, n. 10, p. 7942-7956, 2017
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: green
-
ABNT
ZEVALLOS, Mauricio e GASCO, Loretta e EHLERS, Ricardo Sandes. Riemann manifold Langevin methods on stochastic volatility estimation. Communications in Statistics - Simulation and Computation, v. 46, n. 10, p. 7942-7956, 2017Tradução . . Disponível em: https://doi.org/10.1080/03610918.2016.1255972. Acesso em: 23 abr. 2024. -
APA
Zevallos, M., Gasco, L., & Ehlers, R. S. (2017). Riemann manifold Langevin methods on stochastic volatility estimation. Communications in Statistics - Simulation and Computation, 46( 10), 7942-7956. doi:10.1080/03610918.2016.1255972 -
NLM
Zevallos M, Gasco L, Ehlers RS. Riemann manifold Langevin methods on stochastic volatility estimation [Internet]. Communications in Statistics - Simulation and Computation. 2017 ; 46( 10): 7942-7956.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1080/03610918.2016.1255972 -
Vancouver
Zevallos M, Gasco L, Ehlers RS. Riemann manifold Langevin methods on stochastic volatility estimation [Internet]. Communications in Statistics - Simulation and Computation. 2017 ; 46( 10): 7942-7956.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1080/03610918.2016.1255972 - Comparing multivariate GARCH-DCC models using Hamiltonian Monte Carlo and Stan
- Bayesian estimation of the Kumaraswamy inverse Weibull distribution
- Computational tools for comparing asymmetric GARCH models via Bayes factors
- Outliers identification on spatial models
- Zero variance estimator for GJR-GARCH models via Hamiltonian Monte Carlo
- A Study Hamiltonian Monte Carlo methods in univariate GARCH models
- Modelos de volatilidade estocástica utilizando os métodos de Langevin ajustado Metropolis e de Monte Carlo Hamiltoniano
- Influential observations in spatial models using Bregman divergence
- A study of skewed heavy-tailed distributions as scale mixtures
- Bayesian inference for GJR-GARCH models via Hamiltonian Monte Carlo
Informações sobre o DOI: 10.1080/03610918.2016.1255972 (Fonte: oaDOI API)
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas