A LATIN AMERICAN MARKET ASSET VOLATILITY ANALYSIS: A COMPARISON OF GARCH MODEL, ARTIFICIAL NEURAL NETWORKS AND SUPPORT VECTOR REGRESSION
Victor CHUNG
vchung@unprg.edu.peUniversidad Nacional Pedro Ruiz Gallo, FACFyM (Peru)
https://orcid.org/0000-0002-8358-3939
Jenny ESPINOZA
Universidad Tecnológica del Perú (Peru)
Abstract
The objective of this research was to compare the effectiveness of the GARCH method with machine learning techniques in predicting asset volatility in the main Latin American markets. The daily squared return was utilized as a volatility indicator, and the accuracy of the predictions was assessed using root mean square error (RMSE) and mean absolute error (MAE) metrics. The findings consistently demonstrated that the linear SVR-GARCH models outperformed other approaches, exhibiting the lowest MAE and MSE values across various assets in the test sample. Specifically, the SVRGARCH RBF model achieved the most accurate results for the IPC asset. It was observed that GARCH models tended to produce higher volatility forecasts during periods of heightened volatility due to their responsiveness to significant past changes. Consequently, this led to larger squared prediction errors for GARCH models compared to SVR models. This suggests that incorporating machine learning techniques can provide improved volatility forecasting capabilities compared to the traditional GARCH models.
Keywords:
return, volatility, GARCH, Machine LearningReferences
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
DOI: https://doi.org/10.1016/0304-4076(86)90063-1
Google Scholar
Bezerra, P., Albuquerque, P. (2017). Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels. Computational Management Science, 14, 179–196. https://doi.org/10.1007/s10287-016- 0267-0
DOI: https://doi.org/10.1007/s10287-016-0267-0
Google Scholar
Chen, S., Jeong, K., & Härdle, W. K. (2008). Support vector regression based GARCH model with application to forecasting volatility of financial returns. SFB 649 Discussion SFB 649 Discussion Paper 2008-014. https://dx.doi.org/10.2139/ssrn.2894286
DOI: https://doi.org/10.2139/ssrn.2894286
Google Scholar
Chhajer, P., Shah, M., & Kshirsagar, A. (2022). The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction. Decision Analytics Journal, 2, 100015. https://doi.org/10.1016/j.dajour.2021.100015
DOI: https://doi.org/10.1016/j.dajour.2021.100015
Google Scholar
Christensen, K., Siggaard, M., & Veliyev, B. (2022). A Machine Learning Approach to Volatility Forecasting. Journal of Financial Econometrics, nbac02. https://doi.org/10.1093/jjfinec/nbac020
DOI: https://doi.org/10.1093/jjfinec/nbac020
Google Scholar
Da Silva, I. N., Spatti, D. H., Flauzino, R. A., Liboni, L. H., Reis Alves, S. F. (2016). Artificial Neural Networks: A Practical Course (pp. 3-19). Springer. https://doi.org/10.1007/978-3-319-43162- 8_1
DOI: https://doi.org/10.1007/978-3-319-43162-8_1
Google Scholar
D’Ecclesia, R. L., & Clementi, D. (2021). Volatility in the stock market: ANN versus parametric models. Annals of Operations Research, 299(1), 1101-1127. https://doi.org/10.1007/s10479-019-03374-0
DOI: https://doi.org/10.1007/s10479-019-03374-0
Google Scholar
Feng, H., Kong, F., & Xiao, Y. (2011). Vessel Traffic Flow Forecasting Model Study based on Support Vector Machine. In Shen, G., Huang, X. (eds), Advanced Research on Electronic Commerce, Web Application, and Communication. ECWAC 2011. Communications in Computer and Information Science, (vol. 143, pp. 446 – 451). Springer. https://doi.org/10.1007/978-3-642- 20367-1_72
DOI: https://doi.org/10.1007/978-3-642-20367-1_72
Google Scholar
Filipovic, D., & Khalilzadeh, A. (2021). Machine Learning for Predicting Stock Return Volatility. Swiss Finance Institute Research Paper. 21-95. http://dx.doi.org/10.2139/ssrn.3995529
DOI: https://doi.org/10.2139/ssrn.3995529
Google Scholar
Fraz, T. R., Fatima, S., & Uddin, M. (2022). Modeling and Forecasting Stock Market Volatility of CPEC Founding Countries: Using Nonlinear Time Series and Machine Learning Models. JISR Management and Social Sciences & Economics, 20(1), 1–20. https://doi.org/10.31384/jisrmsse/2022.20.1.1
DOI: https://doi.org/10.31384/jisrmsse/2022.20.1.1
Google Scholar
Gholami, R., Fakhari, N. (2017). Chapter 27 - Support Vector Machine: Principles, Parameters, and Applications. In Samui, P., Sekhar, S., and Balas, V. E., (eds), Handbook of Neural Computation, ( vol. 2017, pp. 515-535) . Academic Press. https://doi.org/10.1016/B978-0-12-811318-9.00027-2
DOI: https://doi.org/10.1016/B978-0-12-811318-9.00027-2
Google Scholar
Karasan, A. & Gaygısız, E. (2022). Volatility Prediction and Risk Management: An SVR-GARCH. SSRN. http://dx.doi.org/10.2139/ssrn.4285524
DOI: https://doi.org/10.2139/ssrn.4285524
Google Scholar
Kristjanpoller, W., Fadic, A., & Minutolo, M. C. (2014). Volatility forecast using hybrid neural network models. Expert Systems with Applications, 41(5), 2437-2442. https://doi.org/10.1016/j.eswa.2013.09.043
DOI: https://doi.org/10.1016/j.eswa.2013.09.043
Google Scholar
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.2307/2975974
DOI: https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
Google Scholar
Bildirici, M., & Ersin, Ö. (2014). Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns. The Scientific World Journal, 2014, 497941. https://doi.org/10.1155/2014/497941
DOI: https://doi.org/10.1155/2014/497941
Google Scholar
Monfared, S. A., & Enke, D. (2014). Volatility Forecasting Using a Hybrid GJR-GARCH Neural Network Model. Procedia Computer Science, 36, 246-253. https://doi.org/10.1016/j.procs.2014.09.087
DOI: https://doi.org/10.1016/j.procs.2014.09.087
Google Scholar
Rodríguez - Vargas, A. (2020). Forecasting Costa Rica inflation with machine learning methods. Latin American Journal of Central Banking, 1,(1-4), 100012. https://doi.org/10.1016/j.latcb.2020.100012
DOI: https://doi.org/10.1016/j.latcb.2020.100012
Google Scholar
Roghani, A. (2016). Artificial Neural Networks: Applications in Financial Forecasting. CreateSpace Independent Publishing Platform.
Google Scholar
Satria, D. (2023). Predicting Banking Stock Prices Using RNN, LSTM, and GRU Approach. Applied Computer Science, 19(1) 82-84. https://doi.org/10.35784/acs-2023-06
DOI: https://doi.org/10.35784/acs-2023-06
Google Scholar
Scholkopf, B., Smola, A. (2018). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Adaptive Computation and Machine Learning series. MIT Press.
DOI: https://doi.org/10.7551/mitpress/4175.001.0001
Google Scholar
Shen, Z., Wan, Q., & Leatham, D. J. (2021). Bitcoin Return Volatility Forecasting: A Comparative Study between GARCH and RNN. Risk and Financial Management, 14(7), 337. https://doi.org/10.3390/jrfm14070337
DOI: https://doi.org/10.3390/jrfm14070337
Google Scholar
Sun, H., & Yu, B. (2020). Forecasting Financial Returns Volatility: A GARCH-SVR Model. Computational Economics, 55, 451–47. https://doi.org/10.1007/s10614-019-09896-w
DOI: https://doi.org/10.1007/s10614-019-09896-w
Google Scholar
Verma, S. (2021). Forecasting volatility of crude oil futures using a GARCH–RNN hybrid approach. Intelligent Systems in Accounting, Finance and Management, 28(2), 130–142. https://doi.org/10.1002/isaf.1489
DOI: https://doi.org/10.1002/isaf.1489
Google Scholar
Wang, L. (2005). Support Vector Machines: Theory and Applications. In Wang, L. (ed.), Studies in Fuzziness and Soft Computing. ( vol. 177). Springer.
DOI: https://doi.org/10.1007/b95439
Google Scholar
Y, X., Wen, X., & Y, X. (2023). Time series prediction and application based on multi-kernel support vector regression. Second International Symposium on Computer Applications and Information Systems, 12721. https://doi.org/10.1117/12.2683400
Google Scholar
Yi, X., Wen, X., & Yin, X. (2023). Time series prediction and application based on multi-kernel support vector regression. Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 12721. https://doi.org/10.1117/12.2683400
DOI: https://doi.org/10.1117/12.2683400
Google Scholar
Yamaka, W., Srichaikul, W., & Maneejuk, P. (2021). Support Vector Machine-Based GARCH-type Models: Evidence from ASEAN-5 Stock Markets. In: Ngoc Thach, N., Kreinovich, V., Trung, N.D. (eds), Data Science for Financial Econometrics. Studies in Computational Intelligence ( vol. 898, pp. 369-381). Springer, https://doi.org/10.1007/978-3-030-48853-6_26
DOI: https://doi.org/10.1007/978-3-030-48853-6_26
Google Scholar
Zahid, M., Iqbal, F., Koutmos, D. (2022). Forecasting Bitcoin Volatility Using Hybrid GARCH Models with Machine Learning. Risks, 10(12), 237. https://doi.org/10.3390/risks10120237
DOI: https://doi.org/10.3390/risks10120237
Google Scholar
Zhang, C., Zhang, Y., Cucuringu, M., & Qian, Z. (2022). Volatility forecasting with machine learning and intraday commonality. arXiv. https://doi.org/10.48550/arXiv.2202.08962
DOI: https://doi.org/10.2139/ssrn.4022147
Google Scholar
Zhang, G. & Qian, G. (2021). Out-of-sample realized volatility forecasting: does the support vector regression compete combination methods. Applied Economics, 53(19), 2192-2205. https://doi.org/10.1080/00036846.2020.1856326
DOI: https://doi.org/10.1080/00036846.2020.1856326
Google Scholar
Authors
Victor CHUNGvchung@unprg.edu.pe
Universidad Nacional Pedro Ruiz Gallo, FACFyM Peru
https://orcid.org/0000-0002-8358-3939
Authors
Jenny ESPINOZAUniversidad Tecnológica del Perú Peru
Statistics
Abstract views: 583PDF downloads: 224
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Islam MOHAMED, Mohamed EL-WAKAD, Khaled ABBAS, Mohamed ABOAMER, Nader A. Rahman MOHAMED, PUPIL DIAMETER AND MACHINE LEARNING FOR DEPRESSION DETECTION: A COMPARATIVE STUDY WITH DEEP LEARNING MODELS , Applied Computer Science: Vol. 20 No. 4 (2024)
- Nataliya SHABLIY, Serhii LUPENKO, Nadiia LUTSYK, Oleh YASNIY, Olha MALYSHEVSKA, KEYSTROKE DYNAMICS ANALYSIS USING MACHINE LEARNING METHODS , Applied Computer Science: Vol. 17 No. 4 (2021)
- Jerzy JÓZWIK, Magdalena ZAWADA-MICHAŁOWSKA, Monika KULISZ, Paweł TOMIŁO, Marcin BARSZCZ, Paweł PIEŚKO, Michał LELEŃ, Kamil CYBUL, MODELING THE OPTIMAL MEASUREMENT TIME WITH A PROBE ON THE MACHINE TOOL USING MACHINE LEARNING METHODS , Applied Computer Science: Vol. 20 No. 2 (2024)
- Shahil SHARMA, Rajnesh LAL, Bimal KUMAR, DEVELOPING MACHINE LEARNING APPLICATION FOR EARLY CARDIOVASCULAR DISEASE (CVD) RISK DETECTION IN FIJI: A DESIGN SCIENCE APPROACH , Applied Computer Science: Vol. 20 No. 3 (2024)
- Anitha Rani PALAKAYALA, Kuppusamy P, A QUALITATIVE AND QUANTITATIVE APPROACH USING MACHINE LEARNING AND NON-MOTOR SYMPTOMS FOR PARKINSON’S DISEASE CLASSIFICATION. A HIERARCHICAL STUDY , Applied Computer Science: Vol. 20 No. 3 (2024)
- Idara JAMES, Veronica OSUBOR, Machine learning evidence towards eradication of malaria burden: A scoping review , Applied Computer Science: Vol. 21 No. 1 (2025)
- Hawkar ASAAD, Shavan ASKAR, Ahmed KAKAMIN, Nayla FAIQ, EXPLORING THE IMPACT OF ARTIFICIAL INTELLIGENCE ON HUMANROBOT COOPERATION IN THE CONTEXT OF INDUSTRY 4.0 , Applied Computer Science: Vol. 20 No. 2 (2024)
- Baigo HAMUNA, Sri PUJIYATI, Jonson Lumban GAOL, Totok HESTIRIANOTO, CLASSIFICATION AND PREDICTION OF BENTHIC HABITAT FROM SCIENTIFIC ECHOSOUNDER DATA: APPLICATION OF MACHINE LEARNING ALGORITHMS , Applied Computer Science: Vol. 20 No. 4 (2024)
- Miguel Angel BELLO RIVERA, Carlos Alberto REYES GARCÍA, Tania Cristal TALAVERA ROJAS, Perfecto Malaquías QUINTERO FLORES, Rodolfo Eleazar PÉREZ LOAIZA, AUTOMATIC IDENTIFICATION OF DYSPHONIAS USING MACHINE LEARNING ALGORITHMS , Applied Computer Science: Vol. 19 No. 4 (2023)
- Malek M. AL-NAWASHI , Obaida M. AL-HAZAIMEH, Mutaz Kh. KHAZAALEH , A NEW APPROACH FOR BREAST CANCER DETECTION- BASED MACHINE LEARNING TECHNIQUE , Applied Computer Science: Vol. 20 No. 1 (2024)
You may also start an advanced similarity search for this article.
