Forecasting realized volatility through financial turbulence and neural networks

Aaltio, J. (2022). Volatility forecasting with artificial neural networks [unpublished PHD dissertation]. Hanken School of Economics. https://helda.helsinki.fi/dhanken/bitstream/handle/10227/509483/Aaltio_Juho.pdf?sequence=1 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Baffour, A. A., Feng, J., & Taylor, E. K. (2019). A hybrid artificial neural network-GJR modeling approach to forecasting currency exchange rate volatility. Neurocomputing, 365, 285–301. https://doi.org/10.1016/j.neucom.2019.07.088 Search in Google Scholar

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 Search in Google Scholar

Black, F. (1986). Noise. Journal of Finance, 41, 529–543. Search in Google Scholar

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1 Search in Google Scholar

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 Search in Google Scholar

Brandt, M. W., & Jones, C. W. (2006). Volatility forecasting with range-based EGARCH models. Journal of Business & Economic Statistics, 24(4), 470–486. https://doi.org/10.1198/073500106000000206 Search in Google Scholar

Bucci, A. (2020). Realized volatility forecasting with neural networks. Journal of Financial Econometrics, 18(3), 502–531. https://doi.org/10.1093/jjfinec/nbaa008 Search in Google Scholar

Chen, Q., & Robert, C. (2022). Multivariate realized volatility forecasting with graph neural network. Proceedings of the Third ACM International Conference on AI in Finance. https://doi.org/10.1145/3533271.3561663 Search in Google Scholar

Chen, W., Yao, J., & Shao, Y. (2022). Volatility forecasting using deep neural network with time-series feature embedding. Ekonomska Istrazivanja–Economic Research, 1377–1401. https://doi.org/10.1080/1331677x.2022.2089192 Search in Google Scholar

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 Search in Google Scholar

Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253–263. https://doi.org/10.2307/1392185 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773 Search in Google Scholar

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 Search in Google Scholar

Gajdka, J., & Pietraszewski, P. (2017). Stock price volatility and fundamental value: Evidence from Central and Eastern European countries. Economics and Business Review, 3(4), 28–46. https://doi.org/10.18559/ebr.2017.4.2 Search in Google Scholar

Garman, M. B., & Klass, M. J. (1980). On the estimation of security price volatilities from historical data. The Journal of Business, 53(1), 67–68. https://doi.org/10.1086/296072 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Harvey, D., Leybourne, S. J., & Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting, 13(2), 281–291. https://doi.org/10.1016/s0169-2070(96)00719-4 Search in Google Scholar

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 Search in Google Scholar

Hu, Y., Ni, J., & Wen, L. (2020). A hybrid deep learning approach by integrating LSTMANN networks with GARCH model for copper price volatility prediction. Physica D: Nonlinear Phenomena, 557, 124907. https://doi.org/10.1016/j.physa.2020.124907 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Karsoliya, S., & Azad, M. (2012). Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. International Journal of Engineering Trends and Technology, 3(6). http://www.ijettjournal.org/volume-3/issue-6/IJETTV3I6P206.pdf Search in Google Scholar

Keras Team. (n.d.). Keras documentation: LSTM layer. Keras.io. https://keras.io/api/layers/recurrent_layers/lstm/ Search 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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Li, J. (2022). The comparison of LSTM, LGBM, and CNN in stock volatility prediction. Proceedings of the 2002 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022). https://doi.org/10.2991/aebmr.k.220307.147 Search in Google Scholar

Li, X., & Wu, X. (2015). Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition. International Conference on Acoustics, Speech, and Signal Processing (ICASSP). https://doi.org/10.1109/icassp.2015.7178826 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Loang, O. K., & Ahmad, Z. (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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Parkinson, M. H. (1980). The extreme value method for estimating the variance of the rate of return. The Journal of Business, 53(1), 61–65. https://doi.org/10.1086/296071 Search in Google Scholar

Rodikov, G., & Antulov-Fantulin, N. (2022). Can LSTM outperform volatility-econometric models? ArXiv Preprint. https://doi.org/10.48550/arXiv.2202.11581 Search 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://www.linkedin.com/pulse/science-behind-openai-five-just-produced-one-greatest-jesus-rodriguez/ Search 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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search 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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search 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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Yan, Y., & Yang, D. (2021). A stock trend forecast algorithm based on deep neural networks. Scientific Programming, 1–7. https://doi.org/10.1155/2021/7510641 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar