Intraday volatility forecasting: analysis of alternative distributions
Main Article Content
Abstract
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 estimate
volatility. 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.
volatility. 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.
Article Details
Issue
Section
Long Paper
Authors that publish in the Brazilian Review of Finance agree with the following terms:
Authors maintain their moral rights and grant to Review the copyright and the first publication right with their work simultaneously licensed under a Creative Commons Attribution 3.0 License that allows the total or partial copy of the work, including for commercial use, and sharing of the work with recognition of its authors and the initial publication in this journal.
Authors are allowed to take additional contracts separately for non-exclusive distribution of the paper published in this journal (eg, in institutional repositories or to publish as a book chapter), with recognition of its authors and the initial publication in this journal.
Authors are permitted and encouraged to post their work online (eg, in institutional repositories or on their website) at any point before or during the submission process, as this can lead to productive exchanges, as well as increase the impact and citation of the published work.
The contents of the signed articles are the sole responsibility of the authors, remains their responsibility even after the transfer of the copyright. The Review reserves the right to make normative, spelling and grammar changes in the original article in order to maintain the linguistic standards, but will follow the style of the authors. RBFin also reserves the right to translate the article into another language. RBFin may impose a copyright transfer form as a condition for publishing the article.
