Forecasting realized volatility through financial turbulence and neural networks
DOI:
https://doi.org/10.18559/ebr.2023.2.737Keywords:
neural networks, LSTM neural networks, realized volatility prediction, financial turbulenceAbstract
This paper introduces and examines a novel realized volatility forecasting model that makes use of Long Short-Term Memory (LSTM) neural networks and the risk metric Financial Turbulence (FT). The proposed model is compared to five alternative models, of which two incorporate LSTM neural networks and the remaining three include GARCH(1,1), EGARCH(1,1), and HAR models. The results of this paper demonstrate that the proposed model yields statistically significantly more accurate and robust forecasts than all other studied models when applied to stocks with middle-to-high volatility. Yet, considering low-volatility stocks, it can only be confidently affirmed that the proposed model yields statistically significantly more robust forecasts relative to all other models considered.
Downloads
References
Aaltio, J. (2022). Volatility Forecasting with Artificial Neural Networks [PhD dissertation]. Hanken School of Economics. https://helda.helsinki.fi/dhanken/bitstream/handle/10227/509483/Aaltio_Juho.pdf?sequence=1
View in Google Scholar
Anders, U., & Korn, O. (1999). Model selection in neural networks. Neural Networks, 12(2), 309–323. https://doi.org/10.1016/s0893-6080(98)00117-8
View in Google Scholar
DOI: https://doi.org/10.1016/S0893-6080(98)00117-8
Andersen, T. M., & Bollerslev, T. (1998). Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts. International Economic Review, 39(4), 885. https://doi.org/10.2307/2527343
View in Google Scholar
DOI: https://doi.org/10.2307/2527343
Arnerić, J., Poklepović, T., & Aljinović, Z. (2014). GARCH based artificial neural networks in forecasting conditional variance of stock returns. Croatian Operational Research Review, 5(2), 329–343. https://doi.org/10.17535/crorr.2014.0017
View in Google Scholar
DOI: https://doi.org/10.17535/crorr.2014.0017
Awais, M., Raza, M., Singh, Y., Bashir, K., Manzoor, U., Islam, S., & Rodrigues, J. J. P. C. (2021). LSTM-Based Emotion Detection Using Physiological Signals: IoT Framework for Healthcare and Distance Learning in COVID-19. IEEE Internet of Things Journal, 8(23), 16863–16871. https://doi.org/10.1109/jiot.2020.3044031
View in Google Scholar
DOI: https://doi.org/10.1109/JIOT.2020.3044031
Baffour, A. A., Feng, J., & Taylor, E. K. (2019). A hybrid artificial neural network-GJR odelling approach to forecasting currency exchange rate volatility. Neurocomputing, 365, 285–301. https://doi.org/10.1016/j.neucom.2019.07.088
View in Google Scholar
DOI: https://doi.org/10.1016/j.neucom.2019.07.088
Bauwens, L., Laurent, S., & Rombouts, J. V. (2006). Multivariate GARCH models: a survey. Journal of Applied Econometrics, 21(1), 79–109. https://doi.org/10.1002/jae.842
View in Google Scholar
DOI: https://doi.org/10.1002/jae.842
Black, F. (1968). Noise. Journal of Finance, 41, 529–543.
View in Google Scholar
DOI: https://doi.org/10.1542/peds.41.2.543
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
View in Google Scholar
DOI: https://doi.org/10.1016/0304-4076(86)90063-1
Borup, D., & Jakobsen, J. S. (2019). Capturing volatility persistence: a dynamically complete realized EGARCH-MIDAS model. Quantitative Finance, 19(11), 1839–1855. https://doi.org/10.1080/14697688.2019.1614653
View in Google Scholar
DOI: https://doi.org/10.1080/14697688.2019.1614653
Bucci, A. (2020). Realized Volatility Forecasting with Neural Networks. Journal of Financial Econometrics, 18(3), 502–531. https://doi.org/10.1093/jjfinec/nbaa008
View in Google Scholar
DOI: https://doi.org/10.1093/jjfinec/nbaa008
Chen, Q., & Robert, C. (2022). Multivariate Realized Volatility Forecasting with Graph Neural Network. ArXiv (Cornell University). https://doi.org/10.1145/3533271.3561663
View in Google Scholar
DOI: https://doi.org/10.1145/3533271.3561663
Chen, W., Yao, J., & Shao, Y. (2022). Volatility forecasting using deep neural network with time-series feature embedding. Ekonomska Istrazivanja-Economic Research, 1–25. https://doi.org/10.1080/1331677x.2022.2089192
View in Google Scholar
DOI: https://doi.org/10.1080/1331677X.2022.2089192
D’Ecclesia, R. L., & Clementi, D. (2021). Volatility in the stock market: ANN versus parametric models. Annals of Operations Research, 299(1–2), 1101–1127. https://doi.org/10.1007/s10479-019-03374-0
View in Google Scholar
DOI: https://doi.org/10.1007/s10479-019-03374-0
Donaldson, R. G., & Kamstra, M. J. (1996a). Forecast combining with neural networks. Journal of Forecasting, 15(1), 49–61. https://doi.org/10.1002/(SICI)1099-131X(199601)15:1<49::AID-FOR604>3.0.CO;2-2
View in Google Scholar
DOI: https://doi.org/10.1002/(SICI)1099-131X(199601)15:1<49::AID-FOR604>3.0.CO;2-2
Donaldson, R. G., & Kamstra, M. J. (1996b). A New Dividend Forecasting Procedure that Rejects Bubbles in Asset Prices: The Case of 1929’s Stock Crash. Review of Financial Studies, 9(2), 333–383. https://doi.org/10.1093/rfs/9.2.333
View in Google Scholar
DOI: https://doi.org/10.1093/rfs/9.2.333
Donaldson, R. G., & Kamstra, M. J. (1997). An artificial neural network-GARCH model for international stock return volatility. Journal of Empirical Finance, 4(1), 17–46. https://doi.org/10.1016/s0927-5398(96)00011-4
View in Google Scholar
DOI: https://doi.org/10.1016/S0927-5398(96)00011-4
Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987. https://doi.org/10.2307/1912773
View in Google Scholar
DOI: https://doi.org/10.2307/1912773
Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock Market Volatility and Macroeconomic Fundamentals. The Review of Economics and Statistics, 95(3), 776–797. https://doi.org/10.1162/rest_a_00300
View in Google Scholar
DOI: https://doi.org/10.1162/REST_a_00300
Gajdka, J., & Pietraszewski, P. (2017). Stock price volatility and fundamental value: evidence from Central and Eastern European countries. Economics and Business Review EBR 17(4), 28-46. https://doi.org/10.18559/ebr.2017.4.2
View in Google Scholar
DOI: https://doi.org/10.18559/ebr.2017.4.2
Garman, M. B., & Klass, M. J. (1980). On the Estimation of Security Price Volatilities from Historical Data. The Journal of Business, 53(1), 67. https://doi.org/10.1086/296072
View in Google Scholar
DOI: https://doi.org/10.1086/296072
Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., & Schmidhuber, J. (2009). A Novel Connectionist System for Unconstrained Handwriting Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(5), 855–868. https://doi.org/10.1109/tpami.2008.137
View in Google Scholar
DOI: https://doi.org/10.1109/TPAMI.2008.137
Hajizadeh, E., Seifi, A., Zarandi, M. H. F., & Turksen, I. (2012). A hybrid modeling approach for forecasting the volatility of S&P 500 index return. Expert Systems With Applications, 39(1), 431–436. https://doi.org/10.1016/j.eswa.2011.07.033
View in Google Scholar
DOI: https://doi.org/10.1016/j.eswa.2011.07.033
Hamid, A., & Iqbal, Z. (2004). Using neural networks for forecasting volatility of S&P 500 Index futures prices. Journal of Business Research, 57(10), 1116–1125. https://doi.org/10.1016/s0148-2963(03)00043-2
View in Google Scholar
DOI: https://doi.org/10.1016/S0148-2963(03)00043-2
Haugom, E., Westgaard, S., Solibakke, P. B., & Lien, G. (2010). Modelling day ahead Nord Pool forward price volatility: Realized volatility versus GARCH models. International Conference on the European Energy Market. https://doi.org/10.1109/eem.2010.5558687
View in Google Scholar
DOI: https://doi.org/10.1109/EEM.2010.5558687
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
View in Google Scholar
DOI: https://doi.org/10.1162/neco.1997.9.8.1735
Hu, M. S., & Tsoukalas, C. (1999). Combining conditional volatility forecasts using neural networks: an application to the EMS exchange rates. Journal of International Financial Markets, Institutions and Money. https://doi.org/10.1016/s1042-4431(99)00015-3
View in Google Scholar
DOI: https://doi.org/10.1016/S1042-4431(99)00015-3
Hu, Y., Ni, J., & Wen, L. (2020). A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction. Physica D: Nonlinear Phenomena, 557, 124907. https://doi.org/10.1016/j.physa.2020.124907
View in Google Scholar
DOI: https://doi.org/10.1016/j.physa.2020.124907
Kambouroudis, D. S., McMillan, D. G., & Tsakou, K. (2016). Forecasting Stock Return Volatility: A Comparison of GARCH, Implied Volatility, and Realized Volatility Models. Journal of Futures Markets, 36(12), 1127–1163. https://doi.org/10.1002/fut.21783
View in Google Scholar
DOI: https://doi.org/10.1002/fut.21783
Kamijo, K., & Tanigawa, T. (1990). Stock price pattern recognition-a recurrent neural network approach. 1990 IJCNN International Joint Conference on Neural Networks. https://doi.org/10.1109/ijcnn.1990.137572
View in Google Scholar
DOI: https://doi.org/10.1109/IJCNN.1990.137572
Karsoliya, S., & Azad, M. (2012). Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture. International Journal of Engineering Trends and Technology. http://www.ijettjournal.org/volume-3/issue-6/IJETT-V3I6P206.pdf
View in Google Scholar
Keras Team. (n.d.). Keras documentation: LSTM layer. Keras.io. https://keras.io/api/layers/recurrent_layers/lstm/
View in Google Scholar
Khan, A. I. (2011). Financial Volatility Forecasting by Nonlinear Support Vector Machine Heterogeneous Autoregressive Model: Evidence from Nikkei 225 Stock Index. International Journal of Economics and Finance. https://doi.org/10.5539/ijef.v3n4p138
View in Google Scholar
DOI: https://doi.org/10.5539/ijef.v3n4p138
Kritzman, M., & Li, Y. (2010). Skulls, Financial Turbulence, and Risk Management. Financial Analysts Journal, 66(5), 30–41. https://doi.org/10.2469/faj.v66.n5.3
View in Google Scholar
DOI: https://doi.org/10.2469/faj.v66.n5.3
Latoszek,M. & Ślepaczuk,R.(2020). Does the inclusion of exposure to volatility into diversified portfolio improve the investment results? Portfolio construction from the perspective of a Polish investor. Economics and Business Review, 6(1), 46–81.https://doi.org/10.18559/ebr.2020.1.3
View in Google Scholar
DOI: https://doi.org/10.18559/ebr.2020.1.3
Li, J. (2022). The Comparison of LSTM, LGBM, and CNN in Stock Volatility Prediction. Advances in Economics, Business and Management Research. https://doi.org/10.2991/aebmr.k.220307.147
View in Google Scholar
DOI: https://doi.org/10.2991/aebmr.k.220307.147
Li, X., & Wu, X. (2015). Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition. ArXiv (Cornell University). https://doi.org/10.1109/icassp.2015.7178826
View in Google Scholar
DOI: https://doi.org/10.1109/ICASSP.2015.7178826
Lin, Y., Lin, Z., Liao, Y., Li, Y., Xu, J., & Yan, Y. (2022). Forecasting the realized volatility of stock price index: A hybrid model integrating CEEMDAN and LSTM. Expert Systems With Applications, 206, 117736. https://doi.org/10.1016/j.eswa.2022.117736
View in Google Scholar
DOI: https://doi.org/10.1016/j.eswa.2022.117736
Liu, R., Demirer, R., Gupta, R., & Tiwari, A. K. (2020). Volatility forecasting with bivariate multifractal models. Journal of Forecasting, 39(2), 155–167. https://doi.org/10.1002/for.2619
View in Google Scholar
DOI: https://doi.org/10.1002/for.2619
Liu, X., Yang, H., Gao, J., & Wang, C. (2021). FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance. Social Science Research Network. https://doi.org/10.2139/ssrn.3955949
View in Google Scholar
DOI: https://doi.org/10.2139/ssrn.3955949
Loang, Ooi Kok, and Zamri Ahmad (2021). Does volatility mediate the impact of analyst recommendations on herding in Malaysian stock market?. Economics and Business Review, 7(4), 54–71. https://doi.org/10.18559/ebr.2021.4.4
View in Google Scholar
DOI: https://doi.org/10.18559/ebr.2021.4.4
Maciel, L., Gomide, F., & Ballini, R. (2016). Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting. Computational Economics, 48(3), 379–398. https://doi.org/10.1007/s10614-015-9535-2
View in Google Scholar
DOI: https://doi.org/10.1007/s10614-015-9535-2
Mayer, H., Gomez, F., Wierstra, D., Nagy, I., Knoll, A., & Schmidhuber, J. (2006). A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks. Advanced Robotics, 22(13–14), 1521–1537. https://doi.org/10.1163/156855308x360604
View in Google Scholar
DOI: https://doi.org/10.1163/156855308X360604
Naidu, G. P., & Govinda, K. (2018). Bankruptcy prediction using neural networks. 2018 2nd International Conference on Inventive Systems and Control (ICISC). https://doi.org/10.1109/icisc.2018.8399072
View in Google Scholar
DOI: https://doi.org/10.1109/ICISC.2018.8399072
Nystrup, P., Boyd, S., Lindström, E., & Madsen, H. (2019). Multi-period portfolio selection with drawdown control. Annals of Operations Research, 282(1–2), 245–271. https://doi.org/10.1007/s10479-018-2947-3
View in Google Scholar
DOI: https://doi.org/10.1007/s10479-018-2947-3
Nystrup, P., Madsen, H., & Lindström, E. (2018). Dynamic portfolio optimization across hidden market regimes. Quantitative Finance, 18(1), 83–95. https://doi.org/10.1080/14697688.2017.1342857
View in Google Scholar
DOI: https://doi.org/10.1080/14697688.2017.1342857
Panchal, G., Ganatra, A., Kosta, Y., & Panchal, D. (2009). Searching most efficient neural network architecture using Akaikes information criterion (AIC). International Journal of Computer Applications, 5, 41–44. https://www.ijcaonline.org/journal/number5/pxc387242.pdf
View in Google Scholar
DOI: https://doi.org/10.5120/126-242
Parkinson, M. H. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. The Journal of Business, 53(1), 61. https://doi.org/10.1086/296071
View in Google Scholar
DOI: https://doi.org/10.1086/296071
Rodikov, G., & Antulov-Fantulin, N. (2022). Can LSTM outperform volatility-econometric models? ArXiv Preprint. https://doi.org/10.48550/arXiv.2202.11581
View in Google Scholar
Rodriguez, J. (2018, July). The Science Behind OpenAI Five that just Produced One of the Greatest Breakthrough in the History of AI. Towards Data Science. https://towardsdatascience.com/the-science-behind-openai-five-that-just-produced-one-of-the-greatest-breakthrough-in-the-history-b045bcdc2b69?gi=24b20ef8ca3f
View in Google Scholar
Rogers, L. C. G., & Satchell, S. (1991). Estimating variance from high, low and closing prices. Annals of Applied Probability, 1(4), 504–512. https://doi.org/10.1214/aoap/1177005835
View in Google Scholar
DOI: https://doi.org/10.1214/aoap/1177005835
Rogers, L. C. G., Satchell, S., & Yoon, Y. (1994). Estimating the volatility of stock prices: a comparison of methods that use high and low prices. Applied Financial Economics, 4(3), 241–247. https://doi.org/10.1080/758526905
View in Google Scholar
DOI: https://doi.org/10.1080/758526905
Rossi, E., & De Magistris, P. S. (2014). Estimation of Long Memory in Integrated Variance. Econometric Reviews, 33(7), 785–814. https://doi.org/10.1080/07474938.2013.806131
View in Google Scholar
DOI: https://doi.org/10.1080/07474938.2013.806131
Sahidullah, M., Patino, J., Cornell, S., Yin, R., Sivasankaran, S., Bredin, H., Korshunov, P., Brutti, A., Serizel, R., Vincent, E., Evans, N., Marcel, S., Squartini, S., & Barras, C. (2019). The speed submission to DIHARD II: Contributions & lessons learned. HAL (Le Centre Pour La Communication Scientifique Directe). https://hal.inria.fr/hal-02352840v2/file/Speed_DIHARDII_Manuscript.pdf
View in Google Scholar
Salisu, A. A., Demirer, R., & Gupta, R. (2022). Financial turbulence, systemic risk and the predictability of stock market volatility. Global Finance Journal, 52, 100699. https://doi.org/10.1016/j.gfj.2022.100699
View in Google Scholar
DOI: https://doi.org/10.1016/j.gfj.2022.100699
Sheela, K. G., & Deepa, S. N. (2013). Review on methods to fix number of hidden neurons in neural networks. Mathematical Problems in Engineering, 425740. https://doi.org/10.1155/2013/425740
View in Google Scholar
DOI: https://doi.org/10.1155/2013/425740
Souto, H.G. (2023a). Distribution analysis of S&P 500 financial turbulence. Journal of Mathematical Finance, 13, 67–88. https://doi.org/10.4236/jmf.2023.131005
View in Google Scholar
DOI: https://doi.org/10.4236/jmf.2023.131005
Souto, H.G. (2023b). Time series forecasting models for S&P 500 financial turbulence. Journal of Mathematical Finance, 13, 112–129. https://doi.org/10.4236/jmf.2023.131007
View in Google Scholar
DOI: https://doi.org/10.4236/jmf.2023.131007
Vidal, A., & Kristjanpoller, W. (2020). Gold volatility prediction using a CNN-LSTM approach. Expert Systems With Applications, 157, 113481. https://doi.org/10.1016/j.eswa.2020.113481
View in Google Scholar
DOI: https://doi.org/10.1016/j.eswa.2020.113481
Vujičić, T. M., Matijević, T., Ljucović, J., Balota, A., & Sevarac, Z. (2016). Comparative analysis of methods for determining number of hidden neurons in artificial neural network. Central European Conference on Information and Intelligent Systems.
View in Google Scholar
White. (1988). Economic prediction using neural networks: the case of IBM daily stock returns. IEEE 1988 International Conference on Neural Networks. https://doi.org/10.1109/icnn.1988.23959
View in Google Scholar
DOI: https://doi.org/10.1109/ICNN.1988.23959
Wilson, R. K., & Sharda, R. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, 11(5), 545–557. https://doi.org/10.1016/0167-9236(94)90024-8
View in Google Scholar
DOI: https://doi.org/10.1016/0167-9236(94)90024-8
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., Klingner, J., Shah, A. S., Johnson, M., Liu, X., Kaiser, Ł., Gouws, S., Kato, Y., Kudo, T., Kazawa, H., ..., Dean, J. (2016). Google’s neural machine translation system: Bridging the gap between human and machine translation. ArXiv. https://arxiv.org/pdf/1609.08144.pdf
View in Google Scholar
Yan, Y., & Yang, D. (2021). A Stock Trend Forecast Algorithm Based on Deep Neural Networks. Scientific Programming, 2021, 1–7. https://doi.org/10.1155/2021/7510641
View in Google Scholar
DOI: https://doi.org/10.1155/2021/7510641
Yang, D., & Zhang, Q. (2000). Drift Independent Volatility Estimation Based on High, Low, Open, and Close Prices. The Journal of Business, 73(3), 477–492. https://doi.org/10.1086/209650
View in Google Scholar
DOI: https://doi.org/10.1086/209650
Zhu, X., Wang, H., Xu, L., & Li, H. (2008). Predicting stock index increments by neural networks: The role of trading volume under different horizons. Expert Systems With Applications, 34(4), 3043–3054. https://doi.org/10.1016/j.eswa.2007.06.023
View in Google Scholar
DOI: https://doi.org/10.1016/j.eswa.2007.06.023
Downloads
Published
Versions
- 2024-06-04 (2)
- 2023-07-20 (1)
Issue
Section
License
Copyright (c) 2023 Hugo Gobato Souto, Amir Moradi
This work is licensed under a Creative Commons Attribution 4.0 International License.
