2021, Vol. 2, Issue 2, Part A
Forecasting performance of hybrid ARIMA- FIGARCH model and hybrid of ARIMA-GARCH model: A comparative study
Author(s): MU Bawa, Dr. HG Dikko, Dr. Anil Shabri, Dr. J Garba and Dr. S Sadiku
Abstract: This paper considers the Comparison of forecasting performance the hybridization between ARIMA Model and Fractionally Integrated Generalized Autoregressive Conditional Heteroscedastic (FIGARCH) processes. With will be used to develop the most appropriate model for forecasting financial Time Series data. The data employed for this study was secondary type in nature for all the variables and it is obtained from the publications of Central Bank of Nigerian bulletin, National Bureau of Statistics and World Bank Statistics Database dated, January, 2005 to Dec, 2019. The result of unit root test shows that all variables are stationary at level and first differences at 5% level of significant. From the Furthermore, the sum of the alpha and beta parameters is close to unity (α + β = 0.9832001), indicating that the persistence of the NSE return is high. Although the returns volatility appears to have what seems to be long memory: the sum of α and β is significantly less than one While the results indicate that the coefficient gamma is not significant, implying that the sign of the innovation has not significant influence on the volatility of returns and also delta is significant it shows the present of long -memory. Based on the results obtaining in table (11) using information’s criteria with shows that AIC, BIC and HQIC of ARIMA-FIGARCH model (14.488, 14.577 and 14.524) are less than for ARIMA-GARCH. With shows that ARIMA-FIGARCH are best model for forecasting National Stock Exchange of Nigeria.
Pages: 48-58 | Views: 976 | Downloads: 526
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How to cite this article:
MU Bawa, Dr. HG Dikko, Dr. Anil Shabri, Dr. J Garba and Dr. S Sadiku. Forecasting performance of hybrid ARIMA- FIGARCH model and hybrid of ARIMA-GARCH model: A comparative study. Journal of Mathematical Problems, Equations and Statistics. 2021; 2(2): 48-58.