A COMPARATIVE STUDY OF STOCK PRICE FORECASTING USING NONLINEAR MODELS

Risk Governance & Control: Financial Markets & Institutions

关键词: Stock Price;    Nonlinear Time Series Models;    Error Metrics;   

DOI  :  10.22495/rgcv7i2art1

学科分类：社会科学、人文和艺术（综合）

来源: Virtus Interpress

【 摘 要 】

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. Nonlinearity tests were used to confirm 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 error measures in evaluating the forecasting ability of the models. The MS-AR models proved to perform well with lower error measures as compared to LSTR and TAR models in most cases.

【 授权许可】

CC BY-NC