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Master's Dissertation
DOI
10.11606/D.104.2017.tde-13112017-160115
Document
Author
Full name
Karen Fiorella Aquino Gutierrez
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2017
Supervisor
Committee
Ehlers, Ricardo Sandes (President)
Herencia, Mauricio Enrique Zevallos
Moura, Maria Sílvia de Assis
Title in Portuguese
Modelagem da volatilidade em séries temporais financeiras via modelos GARCH com abordagem Bayesiana
Keywords in Portuguese
Distribuições assimétricas
Inferência Bayesiana
MCMC
Modelos GARCH
Séries temporais
Volatilidade
Abstract in Portuguese
Nas últimas décadas a volatilidade transformou-se num conceito muito importante na área financeira, sendo utilizada para mensurar o risco de instrumentos financeiros. Neste trabalho, o foco de estudo é a modelagem da volatilidade, que faz referência à variabilidade dos retornos, sendo esta uma característica presente nas séries temporais financeiras. Como ferramenta fundamental da modelação usaremos o modelo GARCH (Generalized Autoregressive Conditional Heteroskedasticity), que usa a heterocedasticidade condicional como uma medida da volatilidade. Considerar-se-ão duas características principais a ser modeladas com o propósito de obter um melhor ajuste e previsão da volatilidade, estas são: a assimetria e as caudas pesadas presentes na distribuição incondicional da série dos retornos. A estimação dos parâmetros dos modelos propostos será feita utilizando a abordagem Bayesiana com a metodologia MCMC (Markov Chain Monte Carlo) especificamente o algoritmo de Metropolis-Hastings.
Title in English
Modeling of volatility in financial time series using GARCH models with Bayesian approach
Keywords in English
Asymmetric distributions
Bayesian inference
GARCH models
MCMC
Time series
Volatility
Abstract in English
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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Publishing Date
2017-11-13
 
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