Nonlinear neural network forecasting model for stock index option price: Hybrid GJR-GARCH approach

This study integrated new hybrid asymmetric volatility approach into artificial neural networks option-pricing model to improve forecasting ability of derivative securities price. Owing to combines the new hybrid asymmetric volatility method can be reduced the stochastic and nonlinearity of the error term sequence and captured the asymmetric volatility simultaneously. Hence, in the ANNS option-pricing model, the results demonstrate that Grey-GJR-GARCH volatility provides higher predictability than other volatility approaches.

[1]  C. Tan,et al.  Option price forecasting using neural networks , 2000 .

[2]  Fabio Fornari,et al.  SIGN- AND VOLATILITY-SWITCHING ARCH MODELS: THEORY AND APPLICATIONS TO INTERNATIONAL STOCK MARKETS , 1997 .

[3]  R. Malhotra,et al.  Evaluating Consumer Loans using Neural Networks , 2003 .

[4]  D. McMillan,et al.  Non-Linear Predictability of Value and Growth Stocks and Economic Activity , 2004 .

[5]  R. Donaldson,et al.  A New Dividend Forecasting Procedure That Rejects Bubbles in Asset Prices: The Case of 1929's Stock Crash , 1996 .

[6]  Burak Saltoǧlu,et al.  Comparing forecasting ability of parametric and non-parametric methods: an application with Canadian monthly interest rates , 2003 .

[7]  H. Daniels,et al.  Estimating structural exchange rate models by artificial neural networks , 1998 .

[8]  Hans-Georg Wittkemper,et al.  Using neural networks to forecast the systematic risk of stocks , 1996 .

[9]  S. Dutta,et al.  Bond rating: a nonconservative application of neural networks , 1988, IEEE 1988 International Conference on Neural Networks.

[10]  Victor L. Berardi,et al.  Time series forecasting with neural network ensembles: an application for exchange rate prediction , 2001, J. Oper. Res. Soc..

[11]  L. Glosten,et al.  On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks , 1993 .

[12]  Angelos Kanas,et al.  Neural network linear forecasts for stock returns , 2001 .

[13]  J. Zakoian Threshold heteroskedastic models , 1994 .

[14]  Linda Salchenberger,et al.  Using neural networks to forecast the S & P 100 implied volatility , 1996, Neurocomputing.

[15]  R. Engle Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[16]  Melody Y. Kiang,et al.  Managerial Applications of Neural Networks: The Case of Bank Failure Predictions , 1992 .

[17]  Min Qi,et al.  Nonlinear prediction of exchange rates with monetary fundamentals , 2003 .

[18]  Cinzia Meraviglia,et al.  Models of representation of social mobility and inequality systems. A neural network approach , 1996, Quality and Quantity.

[19]  S. Hamid,et al.  Using neural networks for forecasting volatility of S&P 500 Index futures prices , 2004 .

[20]  Philip A. Shively The nonlinear dynamics of stock prices , 2003 .

[21]  R. Malhotra,et al.  Evaluating Consumer Loans Using Neural Networks , 2001 .

[22]  Toshiaki Watanabe,et al.  A Non-linear Filtering Approach to Stochastic Volatility Models with an Application to Daily Stock Returns , 1999 .

[23]  I. Kim,et al.  Empirical comparison of alternative stochastic volatility option pricing models: Evidence from Korean KOSPI 200 index options market , 2004 .

[24]  William B. White,et al.  All in the Family , 2005 .

[25]  S. Heston A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options , 1993 .

[26]  Yochanan Shachmurove,et al.  Business Applications of Emulative Neural Networks , 2005 .

[27]  Douglas Wood,et al.  The profitability of daily stock market indices trades based on neural network predictions: case study for the S&P 500, the DAX, the TOPIX and the FTSE in the period 1965–1999 , 2004 .

[28]  Alicia M. Gazely,et al.  A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia , 2005 .

[29]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[30]  Christian L. Dunis,et al.  Forecasting and Trading Currency Volatility: An Application of Recurrent Neural Regression and Model Combination , 2002 .

[31]  David E. Rapach,et al.  Valuation ratios and long-horizon stock price predictability , 2005 .

[32]  C. Granger,et al.  A long memory property of stock market returns and a new model , 1993 .

[33]  David S. Bates Jumps and Stochastic Volatility: Exchange Rate Processes Implicit in Thephlx Deutschemark Options , 1993 .

[34]  Soumitra Dutta,et al.  Bond rating: A non-conservative application of neural networks , 1988 .

[35]  Min Qi,et al.  Nonlinear Predictability of Stock Returns Using Financial and Economic Variables , 1999 .

[36]  A. Kanas,et al.  Comparing linear and nonlinear forecasts for stock returns , 2001 .

[37]  Jeffrey S. Racine,et al.  Entropy and predictability of stock market returns , 2002 .

[38]  John Y. Campbell,et al.  No News is Good News: An Asymmetric Model of Changing Volatility in Stock Returns , 1991 .

[39]  S. Heston,et al.  A Closed-Form GARCH Option Valuation Model , 2000 .

[40]  Edward I. Altman,et al.  Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) , 1994 .

[41]  Daniel B. Nelson CONDITIONAL HETEROSKEDASTICITY IN ASSET RETURNS: A NEW APPROACH , 1991 .

[42]  W. Davidson,et al.  The predictive power of implied volatility: Evidence from 35 futures markets , 2003 .

[43]  Spyros Makridakis,et al.  Accuracy measures: theoretical and practical concerns☆ , 1993 .

[44]  Deng Ju-Long,et al.  Control problems of grey systems , 1982 .

[45]  William E. Griffiths,et al.  The small-sample properties of some preliminary test estimators in a linear model with autocorrelated errors , 1984 .

[46]  F. Black,et al.  The Pricing of Options and Corporate Liabilities , 1973, Journal of Political Economy.

[47]  J. Duan THE GARCH OPTION PRICING MODEL , 1995 .

[48]  M. Medeiros,et al.  Building Neural Network Models for Time Series: A Statistical Approach , 2002 .

[49]  Henrik Amilon A neural network versus Black-Scholes: a comparison of pricing and hedging performances , 2003 .

[50]  Adrian Pagan,et al.  Alternative Models for Conditional Stock Volatility , 1989 .

[51]  Oliver Linton,et al.  A GARCH Model of the Implied Volatility of the Swiss Market Index From Option Pricesdffrom Options Prices , 1998 .

[52]  Chin-Tsai Lin,et al.  The valuation of Taiwan stock index option price - comparison of performances between Black-Scholes and neural network model , 2005 .