Modeling and Forecasting of Realized Volatility: Evidence from Brazil

Abstract

Using intraday data for the most actively traded stocks on the São Paulo Stock Market (BOVESPA) index, this study considers two recently developed models from the literature on the estimation and prediction of realized volatility: the Heterogeneous Autoregressive Model of Realized Volatility (HAR-RV), developed by Corsi (2009), and the Mixed Data Sampling model (MIDAS-RV), developed by Ghysels et al. (2004). Using measurements to compare in-sample and out-of-sample forecasts, better results were obtained with the MIDAS-RV model for in-sample forecasts. For out-of-sample forecasts, however, there was no statistically significant difference between the models. We also found evidence that the use of realized volatility induces distributions of standardized returns that are closer to normal.

Selection of Minimum Variance Portfolio Using Intraday Data: An Empirical Comparison Among Different Realized Measures for BM&FBovespa Data

“…A smaller number of papers focus in the economic applications (Liu, 2009;Hautsch et al, 2013;Wink Junior and Pereira, 2013). As pointed out by Granger and Pesaran (2000), Pesaran and Skouras (2002) and Granger and Machina (2006), when forecasts are used in decision making, it is important to consider the decision process in the ex post evaluation of these forecasts, allowing the interaction between the forecasting model and the objective of the decision making.…”