Modelagem e previsão do valor em risco com modelos de volatilidade baseada em variação: evidências empíricas

• Autor(a) principal: Maciel, Leandro dos Santos
• Data de Publicação: 2017
• Outros Autores: Ballini, Rosangela
• Tipo de documento: Artigo
• Idioma: eng; por
• Título da fonte: Revista Contabilidade & Finanças (Online)
• Texto Completo: https://www.revistas.usp.br/rcf/article/view/138284
• topic: volatilidade; modelos de previsão; mercados financeiros; variação de preço; valor em risco (VaR); volatility; forecasting models; financial markets; price range; value at risk (VaR)

This article considers range-based volatility modeling for identifying and forecasting conditional volatility models based on returns. It suggests the inclusion of range measuring, defined as the difference between the maximum and minimum price of an asset within a time interval, as an exogenous variable in generalized autoregressive conditional heteroscedasticity (GARCH) models. The motivation is evaluating whether range provides additional information to the volatility process (intraday variability) and improves forecasting, when compared to GARCH-type approaches and the conditional autoregressive range (CARR) model. The empirical analysis uses data from the main stock market indexes for the U.S. and Brazilian economies, i.e. S&P 500 and IBOVESPA, respectively, within the period from January 2004 to December 2014. Performance is compared in terms of accuracy, by means of value-at-risk (VaR) modeling and forecasting. The out-of-sample results indicate that range-based volatility models provide more accurate VaR forecasts than GARCH models.