Robustifying Forecast Performance Through Hybridized Arima-Garch-Type Modeling in a Discrete-Time Stochastic Series
Main Article Content
The study is aimed at investigating the robustness of forecast performance of a hybridized (ARIMA-GARCH-type) model over each single component using different periods of horizon to display consistency over time. Daily closing share prices were explored from the Nigerian Stock Exchange for First City Monument Bank and Wema Bank Plc, spanning from January 3, 2006 to December 30, 2016, with a total of 2,713 observations. ARIMA model, GARCH-type, and hybridized ARIMA-GARCH-type were considered. The hybridized ARIMA-GARCH-type was found to produce the best forecast performance in terms of robustness over each single component model and the robustness was found to be consistent over different time horizons for the datasets. The implication is that, it provides an essential remedy to the problem associated with model instability when forecasting a discrete-time stochastic series.
- A. Carriero, A. B. Galvao and G. Kapetanios, A Comprehensive Evaluation of Macroeconomic Forecasting Method, Int. J. Forecast. 35(2019), 1636-1657.
- P. Gorgi, S. J. Koopman and M. Li, Forecasting Economic Time Series using Score-Driven Dynamic Models with Mixed-data Sampling, Int. J. Forecast. 35(2019), 1735-1747.
- E. Granziera and T. Sekhposyan, Predicting Relative Forecasting Performance: An Empirical Investigation, Int. J. Forecast. 35(2019), 1636-1657.
- A. S. Gabriel, Evaluating the Forecasting Performance of GARCH Models: Evidence from Romania, Precedia-Soc. Behav. Sci. 62(2012), 1006-1010.
- H. Leeb, Evaluation and Selection of Models for Out-of-Sample Prediction when the Sample Size is Small Relative to the Complexity of the Data-Generating Process, Bernoulli. 14(2008), 661-690.
- E. Gulay and H. Emec, The Stock Returns Volatility based on the GARCH(1,1) Model: The Superiority of the Truncated Standard Normal Distribution in Forecasting Volatility, Iran. Econ. Rev. 23(2019), 87-108.
- I. U. Moffat and E. A. Akpan, Selection of Heteroscedastic Models: A Time Series Forecasting Approach, Appl. Math. 10(2019), 333-348.
- A. Graefe, K. C. Green and J. S. Armstrong, Accuracy gains from Conservative Forecasting: Tests using Variations of 19 Econometric Models to predict 154 Elections in 10 Countries, PLoS ONE 14(2019), e0209850.
- J. Ding, V. Tarokh and G. Gang, Model Selection Techniques- An Overview, 1EEE Signal Proc. Mag. 21(2018), 1-21.
- M. Pilatowska, Information and Prediction Criteria in Selecting the Forecasting Model, Dyn. Econ. Model. 11(2011), 21-40
- I. Georgiev, D. I. Harvey, S. J. Leybourne and A. M. R. Taylor, Testing for Parameter Instability in Predictive Regression Model, J. Econ. 204(2018), 101-118.
- S. Chen, C. Cui and J. Zhang, On Testing for Structural Break of Coefficients in Factor-Augmented Regression Models, Econ. Lett. 161(2017), 141-145.
- H. Lee, The Effect of Level Shift in the Unconditional Variance on predicting Conditional Volatility, J. Econ. Theory Econometrics, 26(2015), 36-56.
- S. Farhani, Tests of Parameters Instability: Theoretical Study and Empirical Analysis on Two Types of Models (ARMA Model and Market Model), Int. J. Econ. Financial Issues. 2(2012), 246-266.
- H. Zou and Y. Yang, Combining Time Series Models for Forecasting, Int. J. Forecast. 20(2004), 69-84.
- H. Hong, N. Chen, F. O'Brien and J. Ryan, Stock Return Predictability and Model Instability: Evidence from Mainland China and Hong Kong, Q. Rev. Econ. Finance. 68(2018), 132-142.
- D. Pettenuzzo and A. Timmermann, Forecasting Macreconomic Variability under Model Instability, J. Bus. Econ. Stat. 35(2017), 183-201.
- R. Giacomini and B. Rossi, Forecast Comparisons in unstable Environments, J. Appl. Econometrics. 25(2010), 595-620.
- B. Fazelabdolabadi, A Hybrid Bayesian-Network Proposition for Forecasting the Crude Oil Price, Financial Innov. 5(2019), 30.
- E. Spiliotis, F. Fetropoulos and V. Assimakopoulos, Improving the Forecasting Performance of Temporal Hierarchies, PLoS ONE 14(2019), e0223422.
- D. Ardia, K. Bluteau, K. Boudt and L. Catania, Forecasting Risk with Markov-Switching GARCH Models: A Large-Scale Performance Study, Int. J. Forecast. 34(2018), 733-747.
- S. Di Sanzo, A Markov Switching Long Memory Model of Crude Oil Price Return Volatility, Energy Econ. 74(2018), 351-359.
- Y. Runfang, D. Jiangze and L. Xiaotao, Improved Forecast Ability of Oil Market Volatility based on combined Markov Switching and GARCH-class Model, Inform. Technol. Quant. Manage. 122(2017), 415-422.
- S. Gunay, Markov Regime Switching Generalized Autoregressive Conditional Heteroscedastic Model and Volatility Modeling for Oil Returns, Int. J. Energy Econ. Policy. 5(2015), 979-985.
- G.P. Zhang, Time Series Forecasting using a Hybrid ARIMA and Neural Network Model, Neurocomputing. 50(2003), 159-175.
- E.A. Akpan, I. U. Moffat and N. B. Ekpo, Arma-Arch Modeling of the Returns of First Bank of Nigeria, Eur. Sci. J. 12(2016), 257-266.
- E. A. Akpan, K. E. Lasis, A. Adamu and H. B. Rann, Evaluation of Forecasts Performance of ARIMA-GARCH-type Models in the Light of Outliers, World Sci. News. 119(2019), 68-84.
- I.U. Moffat and E. A. Akpan, White Noise Analysis: A Measure of Time Series Model Adequacy, Appl. Math. 10(2019), 989-1003.
- E.A. Akpan and I. U. Moffat, Modeling the Effects of Outliers on the Estimation of Linear Stochastic Time Series Model, Int. J. Anal. Appl. 17(2019), 530-547.
- E. A. Akpan and I. U. Moffat, Detection and Modeling of Asymmetric GARCH Effects in a Discrete-Time Series. Int. J. Stat. Probab. 6(2017), 111-119.
- R. S. Tsay, Analysis of Financial Time Series. (3rded.). New York: John Wiley & Sons Inc., (2010), 97-140.
- R. S. Tsay, Time Series and Forecasting: Brief History and Future Research, J. Amer. Stat. Assoc. 95(2000), 638-643.
- P.H. Franses and D. vanDijk,Non-linear Time Series Models in Empirical Finance. (2nded.). New York, Cambridge University Press, (2003), 135-147.
- I.U. Moffat and E. A. Akpan, Modeling Heteroscedasticity of Discrete-Time Series in the Face of Excess Kurtosis, Glob. J. Sci. Front. Res., F, Math. Decision Sci. 18(2018), 21-32.
- C. Francq and J. Zakoian, GARCH Models: Structure, Statistical Inference and Financial Applications. (1sted.). Chichester, John Wiley &Sons Ltd, (2010) 19-220.
- R.F. Engle and V. K.Ng, Measuring and Testing the Impact of News on Volatility, J. Finance. 48(1993), 1749-1778.
- R.F. Engle, Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflations, Econometrica. 50(1982), 987-1007.
- D.B. Nelson, Conditional Heteroscedasticity of Asset Returns. A new Approach. Econometrica, 59(1991), 347-370.