Bayesian prediction of the stock price rate in the Iraq stock market based on the symmetric heavy tails regression model

Abstract

In this paper, we investigate the estimation of generalized modified Bessel regression model by using the Bayesian techniques under the assumption that the scale parameter and shape parameters are known. We use the informative priors for estimating of model. Then we derive a prediction distribution of the future response variable ▁Y_f by using informative priors for predictive future. Our work applied our results to real data which represent the Iraqi market for securities having taken monthly data for the services sector and of Baghdad sector of Iraq for public transport for the year 2018, as the stock variable rate response variables affecting it are closing price variable, the stock turnover variable. Through the study shows that the explanatory variables are the most important influence on the stock price rate variables through variance inflation factor, the estimated model was appropriate for the data studied.