Estimating Stock Price in Energy Market Including Oil, Gas, and Coal: The Comparison of Linear and Non- Linear Two-State Markov Regime Switching Models

Abstract

A common method to study the dynamic behavior of macroeconomic variables is using linear time series models; however, they are unable to explain nonlinear behavior of the series. Given the dependency between stock market and derivatives, the behavior of the underlying asset price can be modeled using Markov switching process properties and the economic regime significance. In this paper, a two-state Markov switching model in energy market has been examined for oil, coal, and gas since 1991 to 2011. The objective price estimated by the switching model and the parameters were determined by using MATLAB program. With regard to the relationship between the total price and the variables defined in this paper, it is concluded that the non-linear model is relatively better than the linear model, since it has lower RMSE and greater R-squared, therefore it is better to use nonlinear model in Markov switching model for predicting the price of stocks.