Stock Market Prediction Using Nonparametric Fuzzy and Parametric GARCH Methods

Authors : Reza ARABİ BELAGHİ, Minoo AMİNNEJAD, Özlem GÜRÜNLÜ ALMA

Pages : 1-8

Doi:10.34110/forecasting.420126

View : 18 | Download : 13

Publication Date : 2018-09-01

Article Type : Research Paper

Abstract :Prediction of stock market value is one the most complicated issue during the past decades. Due to its importance, in this research, we consider the prediction of stock values based on non-parametric and parametric methods. In this first method, we use the fuzzy Markov chain procedure in order to prediction problem. In this regard, all of the rising and falling probabilities during the weekdays are calculated and then they applied to obtain the increasing and decreasing rate. Then, based on this information we model and predict the stock values. In the sequel, we implement different methods of parametric time series such as generalized autoregressive conditionally heteroskedastic insert ignore into journalissuearticles values(GARCH);, ARIMA-GARCH, Exponential GARCH insert ignore into journalissuearticles values(E-GARCH); and GJR-GARCH by assuming the normal and t-student distribution for the error terms to obtain the best model in terms of minimum mean square errors. Finally, the mythologies developed here are applied for the Tehran Stock Exchange Index insert ignore into journalissuearticles values(TEDPIX);.
Keywords : Conditional variance, Error distribution, Fuzzy prediction, Markov chain, Stock exchange, Volatility models