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

Chih Hsiung Tseng, Sheng Tzong Cheng, Yi Hsien Wang, Jin Tang Peng

研究成果: Article

41 引文 斯高帕斯(Scopus)

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.

原文English
頁(從 - 到)3192-3200
頁數9
期刊Physica A: Statistical Mechanics and its Applications
387
發行號13
DOIs
出版狀態Published - 2008 五月 15

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Condensed Matter Physics

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