Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices

被引：58

作者：

Tseng, Chih-Hsiung ^{[2
]}

Cheng, Sheng-Tzong ^{[2
]}

Wang, Yi-Hsien ^{[1
]}

Peng, Jin-Tang ^{[3
]}

机构：

[1] Yuanpei Univ, Dept Finance, Hsinchu 300, Taiwan

[2] Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, Tainan 70101, Taiwan

[3] Yuanpei Univ, Dept Business Adm, Hsinchu 300, Taiwan

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2008年 / 387卷 / 13期

关键词：

artificial neural networks; EGARCH; grey forecasting model;

D O I：

10.1016/j.physa.2008.01.074

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

This investigation integrates a novel hybrid asymmetric volatility approach into an Artificial Neural Networks option-pricing model to upgrade the forecasting ability of the price of derivative securities. The use of the new hybrid asymmetric volatility method can simultaneously decrease the stochastic and nonlinearity of the error term sequence, and capture the asymmetric volatility. Therefore, analytical results of the ANNS option-pricing model reveal that Grey-EGARCH volatility provides greater predictability than other volatility approaches. (c) 2008 Elsevier B.V. All rights reserved.

引用

页码：3192 / 3200

页数：9

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