Intraday volatility forecasting: analysis of alternative distributions

Volatility forecasting has been of great interest both in academic and professional fields all over the world. However, there is no agreement about the best model to estimatevolatility. New models include measures of skewness, changes of regimes and different distributions; few studies, though, have considered different distributions. This paper aims to investigate how the specification of a distribution influences the performance of volatility forecasting on Ibovespa intraday data, using the APARCH model. The forecasts were carried out assuming six distinct distributions: normal, skewed normal, t-student, skewed t-student, generalized and skewed generalized. The results evidence that the model considering the skewed t-student distribution offered the best fit to the data inside the sample, on the other hand, the model assuming a normal distribution provided a better out-of-the-sample performance forecast.