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Title: Modeling stock markets' volatility using GARCH models with normal, Student's t and stable Paretian distributions
Authors: Curto, J.
Pinto, J. C.
Tavares, G. N.
Keywords: Non-Gaussian distributions
Conditional heteroskedasticity
Issue Date: 2009
Publisher: Springer
Abstract: 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.
Peer reviewed: yes
DOI: 10.1007/s00362-007-0080-5
ISSN: 0932-5026
Accession number: WOS:000262577000006
Appears in Collections:BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica

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