A comparative study of threshold autoregressive, smooth transition regression and markov switching autoregressive models on stock price behaviour
Abstract
This study compared the in-sample forecasting accuracy of three forecasting nonlinear models
namely: the Smooth Transition Regression (STR) model, the Threshold Autoregressive (TAR)
model and the Markov-switching Autoregressive (MS-AR) model. Data used was daily close stock
prices of five banks in the South African banking sector and was obtained from the Johannesburg
Stock Exchange (JSE). It covered the period from 2010 to 2012 with a total of 563 observations.
Nonlinearity and nonstationarity tests used confirmed the validity of the assumptions of the study.
The study used model selection criteria, SBC to select the optimal lag order and for the selection of
appropriate models. The Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean
Square Error (RMSE) served as the enor measures in evaluating the forecasting ability of the
models. The MS-AR models proved to perform well with lower enor measures as compared to
LSTR and TAR models in most cases. The decision by error measures were supported by Diebold
and Mariano test. The findings of the study revealed that the three nonlinear models and forecasting
techniques are good but there is room for further improvement. More specifically, in the case of
TAR and MS-AR, where autoregressive specifications were used, study recommended that the
moving average (MA) and/or autoregressive moving average (ARMA) be used and results
compared with the current results.