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

Research output: Contribution to journalArticle

39 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)3192-3200
Number of pages9
JournalPhysica A: Statistical Mechanics and its Applications
Volume387
Issue number13
DOIs
Publication statusPublished - 2008 May 15

Fingerprint

Stock Index
volatility
Taiwan
Neural Network Model
Volatility
Artificial Neural Network
Option Pricing
Predictability
Error term
forecasting
Forecasting
nonlinearity
Integrate
Nonlinearity
Derivative
Decrease
Model

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Condensed Matter Physics

Cite this

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Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices. / Tseng, Chih Hsiung; Cheng, Sheng Tzong; Wang, Yi Hsien; Peng, Jin Tang.

In: Physica A: Statistical Mechanics and its Applications, Vol. 387, No. 13, 15.05.2008, p. 3192-3200.

Research output: Contribution to journalArticle

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