Modelagem da volatilidade em séries temporais financeiras via modelos GARCH com abordagem bayesiana
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Data
2017-07-18Autor
Aquino Gutierrez, Karen Fiorella
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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.