Abstract
The objective of this paper is to test the forecasting performance of three nonlinear econometric prediction models, namely: Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Exponential Generalized Autoregressive Con- ditional Heteroskedasticity (EGARCH), and the Smooth Transition Autoregressive (SETAR) model applied to the MASI index of the Casablanca Stock Exchange the period studied is from January 01, 2002 to September 20, 2018. Non-linearity tests are used to confirm the study’s hypotheses. The optimal delay was also chosen using Schwartz selection criteria. The Mean Absolute Error (MAE) criterion, the Root Mean Square Error (RMSE) criterion, and the Mean Absolute Percentage Error (MAPE) criterion were used to select the best prediction model. The results of using the GARCH, EGARCH and SETAR models revealed that the SETAR model is the best. These results can be beneficial for financial market traders to make good decisions regarding allocative portfolio and asset management strategies.
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Youness, S., Falloul, M., Smaaine, O., Ahmed, N., Hanaa, H. (2023). Forecasting Performance of GARCH, EGARCH and SETAR Non-linear Models: An Application on the MASI Index of the Casablanca Stock Exchange. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2023. Communications in Computer and Information Science, vol 1863. Springer, Cham. https://doi.org/10.1007/978-3-031-42430-4_34
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