Modelagem da volatilidade em séries temporais financeiras via modelos GARCH com abordagem bayesiana
In the last decades volatility has become a very important concept in the financial area, being used to measure the risk of financial instruments. In this work, the focus of study is the modeling of volatility, that refers to the variability of returns, which is a characteristic present in the finan...
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| Formato: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | Brasil |
| Recursos: | Universidade Federal de São Carlos (UFSCAR) |
| Repositorio: | Repositório Institucional da UFSCAR |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufscar.br:20.500.14289/9340 |
| Acesso em linha: | https://repositorio.ufscar.br/handle/20.500.14289/9340 |
| Access Level: | acceso abierto |
| Palavra-chave: | Séries temporais Inferência bayesiana Volatilidade Modelos GARCH Distribuições assimétricas Time series Bayesian inference Volatility GARCH models Asymmetric distributions CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA |
| Resumo: | In the last decades volatility has become a very important concept in the financial area, being used to measure the risk of financial instruments. In this work, the focus of study is the modeling of volatility, that refers to the variability of returns, which is a characteristic present in the financial time series. As a fundamental modeling tool, we used the GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model, which uses conditional heteroscedasticity as a measure of volatility. Two main characteristics will be considered to be modeled with the purpose of a better adjustment and prediction of the volatility, these are: heavy tails and an asymmetry present in the unconditional distribution of the return series. The estimation of the parameters of the proposed models is done by means of the Bayesian approach with an MCMC (Markov Chain Monte Carlo) methodology , specifically the Metropolis-Hastings algorithm. |
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