Modeling stock markets' volatility using GARCH models with Normal, Student's t and stable Paretian distributions

被引:56
作者
Curto, Jose Dias [1 ]
Pinto, Jose Castro [1 ]
Tavares, Goncalo Nuno [2 ,3 ]
机构
[1] Complexo INDEG ISCTE, ISCTE Business Sch, Dept Quantitat Methods, P-1600189 Lisbon, Portugal
[2] INESC ID, Lisbon, Portugal
[3] Univ Tecn Lisboa, Dept Elect & Comp Engn, Inst Super Tecn, Lisbon, Portugal
关键词
Non-Gaussian distributions; Conditional heteroskedasticity; MAXIMUM-LIKELIHOOD-ESTIMATION; TIME-SERIES MODEL; CONDITIONAL HETEROSKEDASTICITY; SPECULATIVE PRICES; REGRESSION; RETURNS; RATES;
D O I
10.1007/s00362-007-0080-5
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
070103 [概率论与数理统计]; 140311 [社会设计与社会创新];
摘要
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.
引用
收藏
页码:311 / 321
页数:11
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