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Based on studies developed over recent years about the use of high-frequency data for estimating volatility, this article implements the Heterogeneous Autoregressive (HAR) model developed by Andersen, Bollerslev, and Diebold (2007) and Corsi (2009), and the Component (2-Comp) model developed by Maheu and McCurdy (2007) and compare them with the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family models in order to estimate volatility and returns. During the period analyzed, the models using intraday data obtained better returns forecasts of the assets assessed, both in and out-of-sample, thus confirming these models possess important information for a variety of economic agents.

In recent years bonds indexed to inflation rates have experienced a tremendous growth in trading volumes. These securities have become an important tool for the diversification of investors' portfolios, to liability management and especially to gauge the expectations of monetary authorities. In this environment, this study contributes as it presents an amended methodology to estimate the inflation risk premium and in applying different methodologies in the Brazilian market. The results indicate that implicit inflation measures with or without adjustment of the inflation risk premium return the smallest forecast errors in relation to the IPCA of measurement period.

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