Nonlinear neural network forecasting model for stock index option price: Hybrid GJR–GARCH approachYi‐Hsien Wang2007-10-18articleExpert Systems with Applications141 citationsclosedNo readable copy stored yet — this record is metadata only.Stock Market Forecasting MethodsStochastic processes and financial applicationsFinancial Risk and Volatility ModelingAbstractNo abstract was published for this record.Citations in this libraryStored only when both papers are in the corpus — 141 citations exist upstream.References1973The Pricing of Options and Corporate Liabilities1986Generalized autoregressive conditional heteroskedasticity1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation1991Conditional Heteroskedasticity in Asset Returns: A New Approach1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks1993A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options1982Control problems of grey systems1993Measuring and Testing the Impact of News on VolatilityCited by2015Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange2012Neural Networks for Time-Series Forecasting2013AN INVESTOR'S PERSPECTIVE ON INFECTIOUS DISEASES AND THEIR INFLUENCE ON MARKET BEHAVIOR2012Forecasting agricultural output with an improved grey forecasting model based on the genetic algorithm2010Forecasting Exchange Rate in India: An Application of Artificial Neural Network Model2014Parametric models and non-parametric machine learning models for predicting option prices: Empirical comparison study over KOSPI 200 Index options2014Volatility Forecasting Using a Hybrid GJR-GARCH Neural Network Model