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Master's Dissertation
DOI
https://doi.org/10.11606/D.92.2005.tde-11052023-111235
Document
Author
Full name
Marcello Delgado da Silva Paixão
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2005
Supervisor
Committee
Morettin, Pedro Alberto (President)
Canton, Adolpho Walter Pimazoni
Mori, Rogerio
Title in Portuguese
Estimação de volatilidade realizada de Telemar PN com uso de dados de alta freqüência
Keywords in Portuguese
Análise de regressão e de correlação
Análise de séries temporais
Abstract in Portuguese
Essa dissertação tem como objetivo a estimação de volatilidade realizada de Telemar PN para 1 dia e 21 dias úteis, com uso de dados diários e dados de alta frequência, utilizando-se modelagem de séries temporais e modelagem de regressão. A volatilidade implícita foi usada como variável explicativa no modelo de regressão. A conclusão do estudo indica razoável eficácia dos modelos de séries temporais utilizando dados intradiários para previsão de volatilidade realizada de 1 dia e poucos dias úteis quando comparada aquela previsão utilizando dados diários. A modelagem perde eficácia quando o período para o qual se deseja projetar volatilidade cresce, ou seja, a medida em que se aumenta o número de passos à frente.
Title in English
Estimation of realized volatility of Telemar PN using data high frequency
Keywords in English
Regression and correlation analysis
Time series analysis
Abstract in English
The goal of this dissertation is to project realized volatility of Telemar PN, the most liquid Brazilian stock during the period of study, for 1-day and 21-day periods, using daily and high-frequency data sets. The projections were based on time series modeling and regression modeling. It was also used implied volatility, calculated from options traded on Telemar PN, as an explanatory variable. The conclusion of the study was that, while the use of time series models with high frequency data yields good results for short projection periods, it does not generate good results for longer projection periods. The model loses predictability power as the projection horizon increases.
 
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Publishing Date
2023-05-11
 
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