Comparing volatility forecasting models during the global financial crisis

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dc.creatorFrank Magalhães de Pinho
dc.creatorRicardo F. Couto
dc.date.accessioned2022-08-08T12:58:29Z
dc.date.accessioned2025-09-09T01:23:36Z
dc.date.available2022-08-08T12:58:29Z
dc.date.issued2016
dc.identifier.doi10.1080/03610918.2016.1152363
dc.identifier.issn03610918
dc.identifier.urihttps://hdl.handle.net/1843/44029
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofCommunications in Statistics - Simulation and Computation
dc.rightsAcesso Restrito
dc.subjectAdministração
dc.subjectAdministração financeira
dc.subject.otherAPARCH
dc.subject.otherModels Classical
dc.subject.otherBayesian Inference
dc.subject.otherHeavy Tailed Distributions
dc.subject.otherNon-Gaussian state space model
dc.subject.otherVolatility Models
dc.titleComparing volatility forecasting models during the global financial crisis
dc.typeArtigo de periódico
local.citation.epage5270
local.citation.issue0
local.citation.spage5257
local.citation.volume1
local.description.resumoVolatility estimation in financial markets has always been a challenge especially in time of crisis. Once asset prices and investment decisions are highly sensitive to such variable, many different models have been proposed in literature. This article estimates the volatility from a new family of stochastic volatility models called non-Gaussian State Space Models, a subclass of state space models where it is possible to compute exact likelihood. Volatilities of important Asian and Oceanian stock market indexes have been estimated and compared to APARCH model estimates. Results showed that non-Gaussian State Space Models outperformed significantly in both in-sample and forecasting cases.
local.publisher.countryBrasil
local.publisher.departmentFCE - DEPARTAMENTO DE CIÊNCIAS ADMINISTRATIVAS
local.publisher.departmentICX - DEPARTAMENTO DE ESTATÍSTICA
local.publisher.initialsUFMG
local.url.externadoi:10.1080/03610918.2016.1152363

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