Using neural network for forecasting TXO price under different volatility models

Authors:
Ching-Ping Wang;Shin-Hung Lin;Hung-Hsi Huang;Pei-Chen Wu
Affiliations:
Graduate Institute of Finance, Economics, and Business Decision, National Kaohsiung University of Applied Sciences, No. 415, Jiangong Rd., Sanmin District, Kaohsiung City 80778, Taiwan;Department of Finance, National Yunlin University of Science & Technology, No. 123, University Rd., Section 3, Douliou City 64002, Taiwan;Department of Banking and Finance, National Chiayi University, No. 580, Sinmin Rd., Chiayi City 60054, Taiwan;Graduate Institute of Finance, National Pingtung University of Science and Technology, No. 1, Hseuhfu Rd., Neipu, Pingtung 91201, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

This study applies backpropagation neural network for forecasting TXO price under different volatility models, including historical volatility, implied volatility, deterministic volatility function, GARCH and GM-GARCH models. The sample period runs from 2008 to 2009, and thus contains the global financial crisis stating in October 2008. Besides RMSE, MAE and MAPE, this study introduces the best forecasting performance ratio (BFPR) as a new performance measure for use in option pricing. The analytical result reveals that forecasting performances are related to the moneynesses, volatility models and number of neurons in the hidden layer, but are not significantly related to activation functions. Implied and deterministic volatility function models have the largest and second largest BFPR regardless of moneyness. Particularly, the forecasting performance in 2008 was significantly inferior to that in 2009, demonstrating that the global financial crisis during October 2008 may have strongly influenced option pricing performance.