Modeling stock markets’ volatility using GARCH models with Normal, Student’s t ...

• Author(s): Curto, José Dias ; Pinto, José Castro ; Tavares, Gonçalo Nuno
• Date: 2009
• Persistent ID: http://hdl.handle.net/10071/5541
• Origin: Repositório do ISCTE-IUL
• Subject(s): Non-Gaussian distributions; Conditional heteroskedasticity
• Document Type: Article
• Language: English

Description

WOS:000262577000006 (Nº de Acesso Web of Science) As GARCH models and stable Paretian distributions have been revisited in the recent past with the papers of Hansen and Lunde (J Appl Econom 20: 873–889, 2005) and Bidarkota and McCulloch (Quant Finance 4: 256–265, 2004), respectively, in this paper we discuss alternative conditional distributional models for the daily returns of the US, German and Portuguese main stock market indexes, considering ARMA-GARCH models driven by Normal, Student’s t and stable Paretian distributed innovations. We find that a GARCH model with stable Paretian innovations fits returns clearly better than the more popular Normal distribution and slightly better than the Student’s t distribution. However, the Student’s t outperforms the Normal and stable Paretian distributions when the out-of-sample density forecasts are considered.