DEVELOPING THE HYBRID ARIMA- FIGARCH MODEL FOR TIME SERIES ANALYSIS

  • Musa Usman Bawa
  • Hussaini Garba Dikko
  • Jamilu Garba
  • Saidu Sadiku
  • M. Tasi.u
Keywords: FIGARCH, ARIMA hybridizations

Abstract

This study takes into account the newly developed hybrid ARIMA-FIGARCH. We use the daily price index of the S&P 500. The data employed for this study was secondary in nature for all the variables and was obtained from the publications of the Central Bank of Nigeria Bulletin, the National Bureau of Statistics, and the World Bank Statistics Database, dated January 2005 to December 2020. Also, the result of the Jarque-Bera test indicated that the p-values for all variables were less than the alpha level of significance (0.05). Hence, we would reject the null hypothesis that the data for all variables are normally distributed. Also, unit root tests were conducted using ADF and KPSS tests. The result of the ADF test shows that the variable is stationary at a level of 5% significance. That means the variables are integrated in order zero, i.e., 1 (0). And for the KPSS test, 0.881749 is greater than 0.463000, indicating that it is not significant at level 1, indicating that it is not stationary, whereas KPSS is 0.011158, which is less than 0.463000, indicating that it is stationary at level 1. The unit root test is necessary in order to determine the nature of the series and to avoid getting spurious results. We estimate the fractional difference order, d, by the Geweke and Porte-Hudak (GPH) method for testing the present and long memory of the series. The results show that the value...

References

Box, G.E.P., Jenkins, G.M. (1976), Time Series Analysis. Forecasting and Control. San Francisco: Holden-Da Dark, F.(1976). Investigations of financial exchange instability changes. In1976 Proceedings of the Meetings of the Business and Economic Statistics Section; American Statistical Association: Washington, DC, USA, 177-181

Baillie R.T., Long memory processes and fractional integration in econometrics, Journal of Econometrics, 1996, Vol. 73, pp. 5-59 DOI: https://doi.org/10.1016/0304-4076(95)01732-1

Ghysels, E., Santa-Clara, P. & Valkanov, R. (2006). Predicting volatility: Getting the most out of return data sampled at different frequencies. Journal of Econometrics, 131(1-2), 59-95. DOI: https://doi.org/10.1016/j.jeconom.2005.01.004

Engle, R. F., Ghysels, E. and Sohn, B.: 2013, Stock market volatility and macroeconomic fundamentals, Review of Economics and Statistics 95, 776–797. DOI: https://doi.org/10.1162/REST_a_00300

Bollerslev, T.: 1986, Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 31, 307–327 DOI: https://doi.org/10.1016/0304-4076(86)90063-1

Chen, X. and Ghysels, E. (2009). News - good or bad - and its impact on predicting future volatility. Review of Financial Studies (forthcoming). DOI: https://doi.org/10.2139/ssrn.1106725

Tasi’u, M. (2022). Development of symmetric non-linear state-space models. Unpublish Ph.D. Thesis of Department of Statistics, Ahmadu Bello University, Zaria, Nigeria.

Engle, R. F. and Kelly, B.: 2012, Dynamic equicorrelation, Journal of Business and Economic Statistics 30, 212–228 DOI: https://doi.org/10.1080/07350015.2011.652048

Published
2023-07-09
How to Cite
Bawa M. U., Dikko H. G., Garba J., Sadiku S., & Tasi.u M. (2023). DEVELOPING THE HYBRID ARIMA- FIGARCH MODEL FOR TIME SERIES ANALYSIS. FUDMA JOURNAL OF SCIENCES, 7(3), 270 - 274. https://doi.org/10.33003/fjs-2023-0703-1868