Interaction and Pricing between the Taiex Call Options and Spot Market among Different Levels of Moneyness:Application of Bi-Egarch Model and Neuron Algorithm

• Published in: Asia Pacific Management Review Vol. 14; no. 2; pp. 159 - 174
• Main Authors: Hsieha, Tien-shih, Fang, Chen-ling, Goo, Yeon-jia
• Format: Journal Article
• Language: Chinese; English
• Published: 台灣 國立成功大學企業管理學院 01.06.2009; National Cheng Kung University, College of Management
• Subjects: Algorithms; artificial neural network; Derivatives; Futures; GARCH; Hedging; Investments; Literature reviews; Neural networks; Options; Options markets; pricing; Put & call options; Research methodology; Scopus; Securities markets; Stochastic models; Stock exchanges; Trends; TSSCI; Valuation; Volatility
• ISSN: 1029-3132; 2589-8213

This investigation attempts to achieve two objectives. The first aim is to study the relationship between the TAIEX call options market and the spot market among different levels of Moneyness, namely, deep-in-the-money, in-the-money, at-the-money, deep-out-of-the-money, as well as out-of-the-money. The other one is to build a pricing model of TAIEX call options. The experimental data presented in this study come from the daily closing transaction price of TAIEX call options and the associated spot market from September 24, 2001 to August 31, 2003. This investigation applied the Bi_EGARCH model to study the interactive relationship of returns and volatility between TAIEX call options and the spot market, and used the Neuron Algorithm to establish a model for pricing TAIEX call options. This study reaches two main findings. First, the interactive relationship between TAIEX call options and the spot market differs among different Moneyness, and investors take larger risks under out-of-the-money and deep-out-of-money situations. Second, the prediction ability of the neural network pricing model is better than that of the regression model given different levels of Moneyness in most circumstances.