Value-at-risk modeling and forecasting with range-based volatility models : empirical evidence

• Autor(a) principal: Ballini, Rosangela, 1969-
• Data de Publicação: 2017
• Tipo de documento: Artigo
• Idioma: eng; por
• Título da fonte: Repositório da Produção Científica e Intelectual da Unicamp
• Texto Completo: https://hdl.handle.net/20.500.12733/1662477
• topic: Volatilidade (Finanças); Mercados financeiros futuros; Valor em Risco (VaR); Financial volatility; Future financial markets; Value at risk (VaR); Modelos de previsão; Variação de preço; Artigo original

Abstract: 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