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Master's Dissertation
DOI
10.11606/D.45.2008.tde-03052008-170204
Document
Author
Full name
Grazielle Yumi Solda
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2008
Supervisor
Committee
Chiann, Chang (President)
Lopes, Silvia Regina Costa
Toloi, Clelia Maria de Castro
Title in Portuguese
Modelos de memória longa, GARCH e GARCH com memória longa para séries financeiras
Keywords in Portuguese
FIGARCH
GARCH
Memória longa
retornos
variância condicional
volatilidade
Abstract in Portuguese
O objetivo deste trabalho é apresentar e comparar diferentes métodos de modelagem da volatilidade (variância condicional) de séries temporais financeiras. O modelo ARFIMA é empregado para capturar o comportamento de memória longa observado na volatilidade de séries financeiras. Por sua vez, o modelo GARCH é utilizado para modelar a volatilidade variando no tempo destas séries. Finalmente, o modelo FIGARCH é utilizado para modelar a dinâmica dos retornos de séries temporais financeiras juntamente com sua volatilidade. Serão apresentados alguns estimadores para os parâmetros dos modelos estudados. Foram realizadas simulações dos três tipos de modelos com o objetivo de comparar o comportamento dos estimadores para diferentes valores dos parâmetros. Por fim, serão apresentadas aplicações em séries reais.
Title in English
Long memory, GARCH and long memory GARCH models for financial time series
Keywords in English
asset returns
conditional variance
FIGARCH
GARCH
Long memory
volatility
Abstract in English
The goal of this project is to present and compare differents methods of modeling volatility (conditional variance) in financial time series. ARFIMA model is applied to capture long memory behavior of volatility in financial time series. GARCH model is used to model the temporal variation in financial volatility. Finally, FIGARCH model is used to model dynamic of financial time series returns as well as its volatility behavior. We present some estimators for the studied models. Estimators behavior of the three types of models for different parameters is assessed through a simulation study. At last, applications to real data are presented.
 
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Publishing Date
2010-05-26
 
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