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Modelagem da volatilidade em séries temporais financeiras via modelos GARCH com abordagem Bayesiana
| Content Provider | Semantic Scholar |
|---|---|
| Author | Gutierrez, Aquino Fiorella, Karen |
| Copyright Year | 2017 |
| Abstract | K. FIORELLA AQUINO G. Modeling of volatility in financial time series using GARCH models with Bayesian approach. 2017. 84 p. Dissertação (Mestrado em Estatística – Interinstitucional de Pós-Graduação em Estatística) – Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos – SP, 2017. 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. |
| File Format | PDF HTM / HTML |
| DOI | 10.11606/D.104.2017.tde-13112017-160115 |
| Alternate Webpage(s) | https://www.repositorio.ufscar.br/bitstream/handle/ufscar/9340/DissKFAG.pdf?isAllowed=y&sequence=1 |
| Alternate Webpage(s) | https://doi.org/10.11606/D.104.2017.tde-13112017-160115 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
