A Novel Fuzzy Linear Regression Sliding Window GARCH Model for Time-Series Forecasting
Source abstract
Abstract
Generalized autoregressive conditional heteroskedasticity (GARCH) is one of the most popular models for time-series forecasting. The GARCH model uses a maximum likelihood method for parameter estimation. For the likelihood method to work, there should be a known and specific distribution. However, due to uncertainties in time-series data, a specific distribution is indeterminable. The GARCH model is also unable to capture the influence of each variance in the observation because the calculation of the long-run average variance only considers the series in its entirety, hence the information on different effects of the variances in each observation is disregarded. Therefore, in this study, a novel forecasting model dubbed a fuzzy linear regression sliding window GARCH (FLR-FSWGARCH) model was proposed; a fuzzy linear regression was combined in GARCH to estimate parameters and a fuzzy sliding window variance was developed to estimate the weight of a forecast. The proposed model promotes consistency and symmetry in the parameter estimation and forecasting, which in turn increases the accuracy of forecasts. Two datasets were used for evaluation purposes and the result of the proposed model produced forecasts that were almost similar to the actual data and outperformed existing models. The proposed model was significantly fitted and reliable for time-series forecasting.
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Research context
OpenAlex topics: Financial Risk and Volatility Modeling · Fuzzy Systems and Optimization · Complex Systems and Time Series Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Fundamental and Applied Science Department, Universiti Teknologi PETRONAS, Perak 32610, Malaysia
Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA Pahang, Jengka 26400, Pahang, Malaysia
Department of Information System, Universitas Indragiri Indonesia, Tembilahan, Riau 29281, Indonesia
Funding
Malaysian Fundamental Research Grant Scheme (FRGS)
References (42)
Show source-provided references
1. Tobar, F.A., Orchard, M.E., Mandic, D.P., and Constantinides, A.G. (2014, January 27–28). Estimation of Financial Indices Volatility Using a Model with Time-Varying Parameters. Proceedings of the IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), London, UK.10.1109/CIFEr.2014.6924090 ↗
2. Banik, S., and Khan, K. (2015, January 21–23). Forecasting US NASDAQ Stock Index Values Using Hybrid Forecasting Systems. Proceedings of the 18th International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh.10.1109/ICCITechn.2015.7488083 ↗
3. Complex Network Approaches to Nonlinear Time Series Analysis10.1016/j.physrep.2018.10.005 ↗
4. Forecasting Agricultural Products Prices Using Time Series Methods for Crop Planning
5. Dong, M.A. (2010). Tutorial on Nonlinear Time-Series Data Mining in Engineering Asset Health and Reliability Prediction: Concepts, Models, and Algorithms. Math. Probl. Eng., 1–22.10.1155/2010/175936 ↗
6. Generalized Autoregressive Conditional Heteroskedasticity10.1016/0304-4076(86)90063-1 ↗
7. Persistence in Variance, Structural Change, and the GARCH Model10.1080/07350015.1990.10509794 ↗
8. Mosalaosi, M., and Afullo, T.J.O. (2016, January 8–11). Prediction of Asynchronous Impulsive Noise Volatility for Indoor Powerline Communication Systems Using GARCH Models. Proceedings of the Progress in Electromagnetic Research Symposium (PIERS), Shanghai, China.10.1109/PIERS.2016.7735782 ↗
9. Guo, H. (2012, January 10–11). Estimating Volatilities by The GARCH and The EWMA Model of Petrochina and TCL in The Stock Exchange Market of China. Proceedings of the 6th International Scientific Conference Managing and Modelling of Financial Risks, Ostrava, Czech Republic.
10. Modelling and Predicting the Bitcoin Volatility Using GARCH Models
11. On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks10.1111/j.1540-6261.1993.tb05128.x ↗
12. Measuring Extreme Risk of Sustainable Financial System Using GJR-GARCH Model Trading Data-Based10.1016/j.ijinfomgt.2018.12.013 ↗
13. An Artificial Neural Network-GARCH Model for International Stock Return Volatility10.1016/S0927-5398(96)00011-4 ↗
14. Volatility Forecast Based on the Hybrid Artificial Neural Network and GARCH-Type Models10.1016/j.procs.2016.07.145 ↗
15. GO-GARCH: A Multivariate Generalized Orthogonal GARCH Model10.1002/jae.688 ↗
16. The Copula-GARCH Model of Conditional Dependencies: An International Stock Market Application10.1016/j.jimonfin.2006.04.007 ↗
17. Bollerslev, T., Patton, A.J., and Quaedvlieg, R. (2020). Multivariate Leverage Effects and Realized Semi-covariance GARCH Models. J. Econom.10.1016/j.jeconom.2019.12.011 ↗
18. The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes10.1093/rfs/hhn004 ↗
19. Babu, C.N., and Reddy, B.E. (2014, January 10–11). Selected Indian Stock Predictions Using a Hybrid ARIMA-GARCH Model. Proceedings of the International Conference on Advances in Electronics Computers and Communications, Bangalore, India.10.1109/ICAECC.2014.7002382 ↗
20. Ou, P., and Wang, H. (2011, January 6–8). Modeling and Forecasting Stock Market Volatility by Gaussian Processes based on GARCH, EGARCH and GJR Models. Proceedings of the World Congress on Engineering, London, UK.
21. Forecasting the Daily Dynamic Hedge Ratios in Emerging European Stock Futures Markets: Evidence from GARCH Models10.1504/IJBAAF.2019.099316 ↗
22. A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting10.12660/rbfin.v10n3.2012.3871 ↗
23. Nonlinear Neural Network Forecasting Model for Stock Index Option Price: Hybrid GJR–GARCH Approach10.1016/j.eswa.2007.09.056 ↗
24. Volatility Forecast Using Hybrid Neural Network Models10.1016/j.eswa.2013.09.043 ↗
25. Mao, H., Zhu, F., and Cui, Y. (2019). A Generalized Mixture Integer-Valued GARCH Model. Stat. Methods Appl., 1–26.10.1007/s10260-019-00498-2 ↗
26. Lin, Y., Xiao, Y., and Li, F. (2020). Forecasting Crude Oil Price Volatility Via A HM-EGARCH Model. Energy Econ., 104693.10.1016/j.eneco.2020.104693 ↗
27. Shbier, M.Z., Ku-Mahamud, K.R., and Othman, M. (2017, January 17–18). SWGARCH Model for Time Series Forecasting. Proceedings of the 1st International Conference on Internet of Things and Machine Learning, Liverpool, UK.10.1145/3109761.3109806 ↗
28. Developed A Hybrid Sliding Window and GARCH Model for Forecasting of Crude Palm Oil Prices in Malaysia
29. Kapoor, P., and Bedi, S.S. (2013). Weather Forecasting Using Sliding Window Algorithm. ISRN Signal Process., 1–5.10.1155/2013/156540 ↗
30. Forecasting of Nonlinear Time Series Using ANN10.1016/j.fcij.2017.05.001 ↗
31. A Hybrid Fuzzy Time Series Forecasting Model Based on Granular Computing and Bio-Inspired Optimization Approaches10.1016/j.jocs.2018.05.008 ↗
32. A Review of Forecasting Models for New Products
33. A New Fuzzy Time Series Model Based on Robust Clustering for Forecasting of Air Pollution10.1016/j.ecoinf.2017.12.001 ↗
34. A Hybrid ANFIS Model Based on AR and Volatility for TAIEX Forecasting10.1016/j.asoc.2010.04.010 ↗
35. Korkmaz, T., and Aydın, K. (2020, March 12). Using EVMA and GARCH Methods in VaR Calculations: Aplication on ISE-30 Index. Available online: http://content.csbs.utah.edu/~ehrbar/erc2002/pdf/P161.pdf.
36. Matrix Driven Multivariate Fuzzy Linear Regression Model in Car Sales10.3923/jas.2012.56.63 ↗
37. Outlier Detection in Fuzzy Linear Regression About Predicting GDP
38. A Comparison of Fuzzy Regression Methods for The Estimation of the Implied Volatility Smile Function10.1016/j.fss.2014.11.015 ↗
39. Bingham, E., Gionis, A., Haiminen, N., Hiisila, H., Mannila, H., and Terzi, E. (2006, January 20–22). Segmentation and Dimensionality Reduction. Proceedings of the Sixth SIAM International Conference on Data Mining, Bethesda, MD, USA.10.1137/1.9781611972764.33 ↗
40. Datar, M., Gionis, A., Indyk, P., and Motwani, R. (2002, January 6–8). Maintaining Stream Statistics Over Sliding Windows. Proceedings of the Thirteenth Annual ACM-SIAM Symposium on Discrete Algorithms, San Francisco, CA, USA.10.1137/S0097539701398363 ↗
41. A Sliding Windows Approach to Analyse the Evolution of Bank Shares in The European Union10.1016/j.physa.2017.08.095 ↗
42. Kapila, T.R.R., Seneviratna, M.D., Jianguo, W., and Arumawadu, H.I. (November, January 30). A Hybrid Statistical Approach for Stock Market Forecasting Based on Artificial Neural Network and ARIMA Time Series Models. Proceedings of the International Conference on Behavioral, Economic and Socio-cultural Computing (BESC), Nanjing, China.