Forecasting daily volatility using high frequency financial data

• Autor(a) principal: Alves, Thiago Winkler
• Data de Publicação: 2014
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
• Texto Completo: http://hdl.handle.net/10438/11994
• topic: Financial engineering; Volatility forecast; High frequency financial data; Futures market; Engenharia financeira; Previsão de volatilidade; Dados financeiros em alta frequência; Economia; Mercado futuro; Análise de séries temporais; Mercado financeiro - Modelos econométricos

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.