Stock Market Prediction Using Nonparametric Fuzzy and Parametric GARCH Methods

Year 2018, , 1 - 8, 01.09.2018

Reza Arabi Belaghi Minoo Aminnejad Özlem Gürünlü Alma

https://doi.org/10.34110/forecasting.420126

Cited By: 2

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 (GARCH), ARIMA-GARCH,
Exponential GARCH (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 (TEDPIX).

Keywords

Conditional variance, Error distribution, Fuzzy prediction, Markov chain, Stock exchange, Volatility models

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