[
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 Volatility forecasting with artificial neural networks [unpublished PHD dissertation].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