Improving Forecast Accuracy Using Combined Forecasts with Regard to Structural Breaks and ARCH Innovations

Yıl 2022, Sayı: 37, 1 - 25, 29.12.2022

Daud Ali Aser , Esin Firuzan

https://doi.org/10.26650/ekoist.2022.37.1183809

Öz

Accurate forecasts about the future are vital in time series analyses, but accurately modeling complex structures in the data is always challenging. Two major sources of complexity are autoregressive conditional heteroskedasticity (ARCH) effects on data as well as structural breaks in the data, as these affect the quality of data and hence reduce forecast accuracy. In this regard, combining forecast types has been a helpful strategy for improving forecast accuracy for more than 50 years since Bates and Granger’s (1969) original paper. Hence, this paper aims to examine if the gains from combined forecasts are sustained regarding cases with structural breaks and ARCH innovations. Moreover, the study explores which forecast combination schemes are optimal for those cases by combining the exponential smoothing (ETS), autoregressive integrated moving average (ARIMA), and artificial neural network (ANN) forecast models using simple and regression-based combination procedures. These methods are implemented in both simulated series and over empirical data from two popular Turkish stock exchanges (i.e., BIST-30 and BIST-100 Indexes). The study has found regression- based forecast combination methods to significantly improve forecast accuracy regarding cases with structural breaks and conditional heteroscedasticity. Dynamically weighted combinations show greater accuracy improvement compared to their static counterparts when the data contain a trend. Simple combination schemes, including simple averages, just perform better than single methods for ETS and ARIMA, while they barely outperform ANN. In conclusion, ANN is found to be the best-performing individual forecasting method for all cases and designs.

Anahtar Kelimeler

Structural Break, Forecasts Combination, ARCH Effects, Artificial Neural Networks

Kaynakça

• AKER, Y. (2022). Analysis of Price Volatility in BIST 100 Index With Time Series: Comparison of Fbprophet and LSTM Model. European Journal of Science and Technology, 35, 89–93. https:// doi.org/10.31590/ejosat.1066722.
• ALP, S., YİĞİT, Ö. E., & ÖZ, E. (2020). Prediction Of BIST Price Indices: A Comparative Study Between Traditional and Deep Learning Methods. Sigma Journal of Engineering and Natural Sciences, 38(4), 1693 – 1704.
• Andrawis, R. R., Atiya, A. F., & El-Shishiny, H. (2011). Combination of long-term and short-term forecasts, with application to tourism demand forecasting. International Journal of Forecasting, 27(3), 870–886. https://doi.org/10.1016/j.ijforecast.2010.05.019.
• Aygören, H., Sarıtaş, H., & Moralı, T. (2012). İMKB 100 Endeksinin Yapay Sinir Ağları ve Newton Nümerik Arama Modelleri ile Tahmini. Uluslararası Alanya İşletme Fakültesi Dergisi, 4(1), 73–88.
• Bai, J. and Perron, P. (2003). Computation and Analysis of Multiple Structural Change Models. Journal of Applied Econometrics 18 (1): 1–22.
• Bai, J. and Perron, P. (1998). Estimating and Testing Linear Models with Multiple Structural Changes. The Econometric Society, 66(1), 47–78.
• Bates, A. J. M., & Granger, C. W. J. (1969). The Combination of Forecasts Stable URL : http:// www.jstor.org/stable/3008764 REFERENCES Linked references are available on JSTOR for this article : The Combination of Forecasts. 20(4), 451–468.
• Box, G. and Jenkins, G. (1970). Time series analysis: forecasting and control. Holden-Day.
• Claeskens, G., Magnus, J. R., Vasnev, A. L., & Wang, W. (2016). The forecast combination puzzle: A simple theoretical explanation. International Journal of Forecasting, 32(3), 754–762. https:// doi.org/10.1016/j.ijforecast.2015.12.005.
• Clemen, R. T. (1989). Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5(4), 559–583. https://doi.org/10.1016/0169-2070(89)90012-5.
• Clements, M.P. and Hendry, D.F. (1998). Forecasting Economic Time Series, Cambridge University Press.
• Clements, M.P. and Hendry D.F. (999). Forecasting Non-stationary Economic Time Series, The MIT Press.
• Crane, D. B. and Crotty, J. R. (1967). A two-stage forecasting model: Exponential smoothing and multiple regression. Management Science, 13(8):501–507. https://doi.org/10.1287/ mnsc.13.8.B501.
• Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50 (4): 987-1007.
• Granger, C. W. and Ramanathan, R. (1984). Improved methods of combining forecasts. Journal of Forecasting, 3 (2):197–204,. URL https://doi.org/10.1002/for.3980030207.
• Hansen, B. E., 2001. The New Econometrics of Structural Change. Journal of Economic Perspectives, 15(4), 117–128.
• Hansen, B. E. (2005). Challenges for econometric model selection. Econometric Theory, 21(1), 60–68. https://doi.org/10.1017/S0266466605050048
• Holt, C. C., (2004). Forecasting seasonals and trends by exponentially weighted moving averages. 20, 5–10. https://doi.org/10.1016/j.ijforecast.2003.09.015
• Hsiao, C., & Wan, S. K. (2014). Is there an optimal forecast combination? Journal of Econometrics, 178(PART 2), 294–309. https://doi.org/10.1016/j.jeconom.2013.11.003
• Hyndman, R.J.,(2012). New in forecast 4.0. December 2012. https://robjhyndman.com/hyndsight/ forecast4/
• Hyndman, R. J., Khandakar, Y., (2008). Automatic Time Series Forecasting : The forecast Package for R. Journal of Statistical Software, 27(3). https://doi: 10.18637/jss.v027.i03
• Hyndman, R. J., & Athanasopoulos, G., (2018). Forecasting : Principles and Practice. , OTexts, Melbourne. OTexts.com/fpp2.
• Koop, G., & Potter, S., (2001). Are Apparent Findings of Nonlinearity Due to Structural Instability in Economic Time Series? The Econometrics Journal, 4(1),37–55. https://doi.org/10.2139/ ssrn.163151.
• Nowotarski, J., Raviv, E., Trück, S., & Weron, R. (2014). An empirical comparison of alternative schemes for combining electricity spot price forecasts. Energy Economics, 46, 395–412. https:// doi.org/10.1016/j.eneco.2014.07.014.
• PAKEL, C., & ÖZEN, K. (2021). Daily Volatility Analysis of Bist 100 Constituents Between 2018-2020. M U Iktisadi ve Idari Bilimler Dergisi, 42(2), 340–360. https://doi.org/10.14780/ muiibd.854509.
• Palm, F. C. and Zellner, A. (1992). To combine or not to combine? issues of combining forecasts. Journal of Forecasting, 11(8):687–701. https://doi.org/10.1002/for.3980110806.
• Raşo, H., & Demirci, M. (2019). Predicting the Turkish Stock Market BIST 30 Index using Deep Learning. International Journal of Engineering Research and Development, 11(1), 253 - 265. https://doi.org/10.29137/umagd.425560.
• Siliverstovs, B., & van Dijk, D., (2003). Forecasting Industrial Production with Linear, Nonlinear, and Structural Change Models. Econometric Institute Report EI 2003-16.
• Smith, J., & Wallis, K. F. (2009). A simple explanation of the forecast combination puzzle. Oxford Bulletin of Economics and Statistics, 71(3), 331–355. https://doi.org/10.1111/j.1468- 0084.2008.00541.x
• Stock, J. H., & Watson, M. W. (2004). Combination forecasts of output growth in a seven-country data set. Journal of Forecasting, 23(6), 405–430. https://doi.org/10.1002/for.928
• Stock, J. H., & Watson, M. W. (1996). Evidence on Structural Instability in Macroeconomic Time Series Relations. Journal of Business & Economic Statistics,14:3, 11–30
• Tellİ, Ş., &COŞKUN, M. (2016). Forecasting the BIST 100 Index Using Artificial Neural Networks with Consideration of the Economic Calendar Forecasting the BIST 100 Index Using Artificial Neural Networks. International Review of Economics and Management, 4(3), 26–46. https:// doi.org/10.18825/irem.67309.
• Tsay, R. (2005). Analysis of Financial Time Series, 2nd ed. (Wiley).
• ÜNVAN, Y. A., & ERGENÇ, C. (2022). Forecasting BIST 100 Index With Artificial Neural Networks and Regression Analysis. BİLTÜRK Journal of Economics and Related Studies, 4(1), 20–32. https://doi.org/10.47103/bilturk.1039669.
• Weiss, C. E., Raviv, E., & Roetzer, G. (2019). Forecast combinations in R using the ForecastComb package. R Journal, 10(2), 262–281. https://doi.org/10.32614/RJ-2018-052
• Winkler, R. L., & Makridakis, S. (1983). The Combination of Forecasts. Journal of the Royal Statistical Society. Series A (General), 146(2), 150. https://doi.org/10.2307/2982011
• Winters, P.R., (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324-342. https://www.jstor.org/stable/2627346.
• Yılmaz, K., & Kale, S. (2022). Risk ve Finansal Göstergeler Arasindaki Asimetrik İlişki: BIST İmalat Sektöründe Bir Uygulama. Ekoist: Journal of Econometrics and Statistics, 0(0), 0–0. https://doi.org/10.26650/ekoist.2022.36.1035097