Forecasting daily volatility using high frequency financial data

• Ano de defesa: 2014
• Autor(a) principal: Alves, Thiago Winkler
• Orientador(a): Ruilova Terán, Juan Carlos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Palavras-chave em Português: Engenharia financeira; Previsão de volatilidade; Dados financeiros em alta frequência
• Palavras-chave em Inglês: Financial engineering; Volatility forecast; High frequency financial data; Futures market
• Link de acesso: http://hdl.handle.net/10438/11994

Aiming at empirical findings, this work focuses on applying the HEAVY model for daily volatility with financial data from the Brazilian market. Quite similar to GARCH, this model seeks to harness high frequency data in order to achieve its objectives. Four variations of it were then implemented and their fit compared to GARCH equivalents, using metrics present in the literature. Results suggest that, in such a market, HEAVY does seem to specify daily volatility better, but not necessarily produces better predictions for it, what is, normally, the ultimate goal. The dataset used in this work consists of intraday trades of U.S. Dollar and Ibovespa future contracts from BM&FBovespa.