El uso de machine learning en volatilidad: una revisión usando K-means

DOI:  https://doi.org/10.12804/revistas.urosario.edu.co/empresa/a.11969

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Publicado: jun. 23, 2023
Número: Vol. 25 Núm. 44 (2023)
Sección Artículo de revisión


Palabras clave: análisis bibliometrico k means literatura financiera machine learning volatilidad

Contenido principal del artículo

Autores/as

  Jesus Enrique Molina Muñoz
Universidad del Rosario
Ricard Castañeda
Universidad del Rosario

Resumen

Recientemente, el uso de técnicas de machine learning (ML) en diferentes disciplinas científicas ha experimentado un aumento sin precedentes. El área de las finanzas no ha sido una excepción. En los últimos años, se han publicado numerosos trabajos utilizando técnicas de ML. Sin embargo, uno de los temas con menor número de artículos desarrollados en este contexto, es el de la volatilidad. A pesar de los anterior, los datos analizados en este articulo sugieren cambios al respecto. Datos obtenidos de la base Web of Science muestran entre 2001 y 2010 había 33 artículos asociados con este tema. Sorprendentemente, entre 2019 y 2023 se han publicado 189 manuscritos relacionados con este tipo de modelos. El propósito de este artículo es revisar los trabajos relacionados con las aplicaciones de ml en volatilidad. Para ello, se propone una clasificación de las principales propuestas sobre esta
temática siguiendo una metodología narrativa, acompañada de un análisis estadístico y bibliométrico en el que se utilizan técnicas novedosas como K-means. Los resultados son sugerentes. Aunque la mayoría de los artículos se centran en la predicción de la volatilidad a través de redes neuronales y support vector machines, se evidencia una ausencia de artículos relacionados con transmisión de la volatilidad, calibración de superficies de volatilidad, y finanzas corporativas. Además, los resultados obtenidos indican que se presentan vacíos en la producción de trabajos relacionados con estos tópicos en revistas especializadas en finanzas y economía.

Cómo citar
Molina Muñoz, J. E., & Castañeda, R. (2023). El uso de machine learning en volatilidad: una revisión usando K-means. Revista Universidad Y Empresa, 25(44), 1–28. https://doi.org/10.12804/revistas.urosario.edu.co/empresa/a.11969
Referencias bibliográfica

Al-Fattah, S. M. (2019). Artificial intelligence approach for modeling and forecasting oilprice

volatility. spe Reservoir Evaluation & Engineering, 22(03), 817-826. https://doi.org/10.2118/195584-PA

Amornwattana, S., Enke, D., & Dagli, C. H. (2007). A hybrid option pricing model using a neural network for estimating volatility. International Journal of General Systems, 36(5), 558-573. https://doi.org/10.1080/03081070701210303

Aria, M. & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007

Arvin, R., Khattak, A. J., & Qi, H. (2021). Safety critical event prediction through unified analysis

of driver and vehicle volatilities: Application of deep learning methods. Accident Analysis & Prevention, 151, 105949. https://doi.org/10.1016/j.aap.2020.105949

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

Bekiros, S. D., & Georgoutsos, D. A. (2008). Direction‐of‐change forecasting using a volatility‐

based recurrent neural network. Journal of Forecasting, 27(5), 407-417. https://doi.org/10.1002/for.1063

Bildirici, M., & Ersin, Ö. (2015). Forecasting volatility in oil prices with a class of nonlinear volatility models: smooth transition rbf and mlp neural networks augmented garch approach.

Petroleum Science, 12, 534-552. https://doi.org/10.1007/s12182-015-0035-8

Bouteska, A., Hajek, P., Fisher, B., & Abedin, M. Z. (2023). Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network. Research in International Business and Finance, 64, 101863. https://doi.org/10.1016/j.ribaf.

101863

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

Cai, X., Lai, G., & Lin, X. (2013). Forecasting large scale conditional volatility and covariance using neural network on gpu. The Journal of Supercomputing, 63, 490-507. https://doi.org/10.1007/s11227-012-0827-1

Calôba, L. O. M., Calôba, L. P., & Contador, C. R. (2001). Delta–Neutral Volatility Trading using Neural Networks F3. International Journal of Engineering Intelligent Systems, 9(4), 243-249. https://lps.ufrj.br/~caloba/Papers%20meus/forecasting_market_volatility.pdf

Cao, J., Chen, J., & Hull, J. (2020). A neural network approach to understanding implied volatility

movements. Quantitative Finance, 20(9), 1405-1413. https://doi.org/10.1080/14697688.2020.1750679

Chen, S., Härdle, W. K., & Jeong, K. (2010). Forecasting volatility with support vector machinebased garch model. Journal of Forecasting, 29(4), 406-433. https://doi.org/10.1002/for.1134

Chkili, W., & Hamdi, M. (2021). An artificial neural network augmented garch model for Islamic

stock market volatility: Do asymmetry and long memory matter? International Journal of Islamic and Middle Eastern Finance and Management, 14(5), 853-873. https://doi.org/10.1108/IMEFM-05-2019-0204

Chung, S. S., & Zhang, S. (2017). Volatility estimation using support vector machine: Applications to major foreign exchange rates. Electronic Journal of Applied Statistical Analysis, 10(2), 499-511. http://siba-ese.unisalento.it/index.php/ejasa/article/view/17080/15510

Dash, G. H., & Kajiji, N. (2008). Engineering a generalized neural network mapping of volatility

spillovers in European government bond markets. In C. Zopounidis, M. Doumpos, & P. M. Pardalos (Eds.), Handbook of Financial Engineering (pp. 201-230). Springer. https://

doi.org/10.1007/978-0-387-76682-9_7

Di-Giorgi, G., Salas, R., Avaria, R., Ubal, C., Rosas, H., & Torres, R. (2023). Volatility forecasting

using deep recurrent neural networks as garch models. Computational Statistics, 1-27.

https://doi.org/10.1007/s00180-023-01349-1

Dixon, M. F., Halperin, I., & Bilokon, P. (2020). Machine learning in finance: From Theory to

practice. Springer International Publishing. https://doi.org/10.1007/978-3-030-41068-1

Enke, D., & Thawornwong, S. (2005). The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications, 29(4), 927-940. https://doi.org/10.1016/j.eswa.2005.06.024

Ewees, A. A., Abd Elaziz, M., Alameer, Z., Ye, H., & Jianhua, Z. (2020). Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility. Resources Policy, 65, 101555. https://doi.org/10.1016/j.resourpol.

101555

Fadda, S. (2020). Pricing options with dual volatility input to modular neural networks. Borsa

Istanbul Review, 20(3), 269-278. https://doi.org/10.1016/j.bir.2020.03.002

Ferrari, R. (2015). Writing narrative style literature reviews. Medical Writing, 24(4), 230-235.

https://doi.org/10.1179/2047480615Z.000000000329

Fisher, I. E., Garnsey, M. R., & Hughes, M. E. (2016). Natural language processing in accounting,

auditing and finance: A synthesis of the literature with a roadmap for future research.

Intelligent Systems in Accounting, Finance and Management, 23(3), 157-214. https://doi.org/10.1002/isaf.1386

Fong, B., Fong, A. C. M., Hong, G. Y., & Wong, L. (2005, December). An empirical study of volatility predictions: Stock market analysis using neural networks. In X. Deng & Y. Ye (Eds.), Internet and Network Economics. WINE 2005. Lecture Notes in Computer Science

(pp. 473-480). Springer. https://doi.org/10.1007/11600930_47

Ge, H., Xu, G., Huang, J., & Ma, X. (2019). A mine main fans switchover system with lower air flow volatility based on improved particle swarm optimization algorithm. Advances in Mechanical Engineering, 11(3). https://doi.org/10.1177/1687814019829281

Ghoddusi, H., Creamer, G. G., & Rafizadeh, N. (2019). Machine learning in energy economics

and finance: A review. Energy Economics, 81, 709-727. https://doi.org/10.1016/j.eneco.

05.006

Gong, X. L., Liu, X. H., Xiong, X., & Zhuang, X. T. (2019). Forecasting stock volatility process

using improved least square support vector machine approach. Soft Computing, 23, 11867-

https://doi.org/10.1007/s00500-018-03743-0

Gupta, R., Nel, J., & Pierdzioch, C. (2021). Investor confidence and forecastability of us stock

market realized volatility: Evidence from machine learning. Journal of Behavioral Finance, 24(1), 111-122. https://doi.org/10.1080/15427560.2021.1949719

Hamid, S. 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

Hein, P. H., Kames, E., Chen, C., & Morkos, B. (2021). Employing machine learning techniques to assess requirement change volatility. Research in Engineering Design, 32, 245-269.

https://doi.org/10.1007/s00163-020-00353-6

Horvath, B., Muguruza, A., & Tomas, M. (2021). Deep learning volatility: a deep neural network

perspective on pricing and calibration in (rough) volatility models. Quantitative Finance, 21(1), 11-27. https://doi.org/10.1080/14697688.2020.1817974

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 A: Statistical Mechanics and its Applications, 557, 124907. https://doi.org/10.1016/j.physa.2020.124907

Hung, J. C. (2011). Adaptive Fuzzy-garch model applied to forecasting the volatility of stock

markets using particle swarm optimization. Information Sciences, 181(20), 4673-4683.

https://doi.org/10.1016/j.ins.2011.02.027

Hung, J. C. (2015). Robust Kalman filter based on a fuzzy garch model to forecast volatility using particle swarm optimization. Soft Computing, 19, 2861-2869. https://doi.org/10.1007/s00500-014-1447-x

Jang, Y., Son, J., & Yi, J. S. (2021). Classifying the level of bid price volatility based on machine

learning with parameters from bid documents as risk factors. Sustainability, 13(7), 3886. https://doi.org/10.3390/su13073886

Jerbi, Y., & Chaabene, S. (2020). European call price modelling using neural networks in considering volatility as stochastic with comparison to the Heston model. Journal of Statistical

Computation and Simulation, 90(10), 1793-1810. https://doi.org/10.1080/00949655.2020.1747463

Jia, F., & Yang, B. (2021). Forecasting volatility of stock index: Deep learning model with likelihood-based loss function. Complexity. https://doi.org/10.1155/2021/5511802

Jobejarkol, M. P., Badamchizadeh, A., & Morales, M. (2018). Implied volatility parameterization

based on a machine learning polynomial approach. Intelligent Data Analysis, 22(5), 1127-1141. https://doi.org/10.3233/IDA-173600

Jung, G., & Choi, S. Y. (2021). Forecasting foreign exchange volatility using deep learning autoencoder-lstm techniques. Complexity. https://doi.org/10.1155/2021/6647534

Kakade, K., Mishra, A. K., Ghate, K., & Gupta, S. (2022). Forecasting commodity market returns

volatility: A hybrid ensemble learning garch‐lstm based approach. Intelligent Systems in Accounting, Finance and Management, 29(2), 103-117. https://doi.org/10.1002/isaf.1515

Kamdem, J. S., Essomba, R. B., & Berinyuy, J. N. (2020). Deep learning models for forecasting

and analyzing the implications of covid-19 spread on some commodities markets volatilities.

Chaos, Solitons & Fractals, 140, 110215. https://doi.org/10.1016/j.chaos.2020.110215

Khashman, A., & Nwulu, N. I. (2011) Support vector machines versus back propagation algorithm for oil price prediction. In D. Liu, H. Zhang, M. Polycarpou, C. Alippi, & H. He, (Eds), Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6677 (pp. 530-538) Springer. https://doi.org/10.1007/978-3-642-21111-9_60

Kaushik, R., Jain, S., Jain, S., & Dash, T. (2019). Performance evaluation of deep neural networks

for forecasting time-series with multiple structural breaks and high volatility. caai Transactions on Intelligence Technology, 6(3), 265-280. https://doi.org/10.1049/cit2.12002

Kim, J., & Baek, C. (2018). Neural network heterogeneous autoregressive models for realized

volatility. Communications for Statistical Applications and Methods, 25(6), 659-671.

https://doi.org/10.29220/CSAM.2018.25.6.659

Kim, Y., & Enke, D. (2018). A dynamic target volatility strategy for asset allocation using artificial neural networks. The Engineering Economist, 63(4), 273-290. https://doi.org/10.1080/0013791X.2018.1461287

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

Kristjanpoller, W., & Minutolo, M. C. (2015). Gold price volatility: A forecasting approach using

the artificial neural network–garch model. Expert Systems with Applications, 42(20), 7245-7251. https://doi.org/10.1016/j.eswa.2015.04.058

Kristjanpoller, W., & Minutolo, M. C. (2016). Forecasting volatility of oil price using an artificial

neural network-garch model. Expert Systems with Applications, 65, 233-241. https://doi.org/10.1016/j.eswa.2016.08.045

Kristjanpoller, W., & Minutolo, M. C. (2018). A hybrid volatility forecasting framework integrating garch, artificial neural network, technical analysis and principal components analysis. Expert Systems with Applications, 109, 1-11. https://doi.org/10.1016/j.eswa.2018.05.011

Kwak, S., Hwang, Y., Choi, Y., Wang, J., Kim, S., & Kim, J. (2022). Reconstructing the local volatility surface from market option prices. Mathematics, 10(14), 2537. https://doi.org/10.3390/math10142537

Kyoung-Sook, M. O. O. N., & Hongjoong, K. I. M. (2019). Performance of deep learning in prediction of stock market volatility. Economic Computation & Economic Cybernetics Studies

& Research, 53(2). https://doi.org/10.24818/18423264/53.2.19.05

Lantz, B. (2019). Machine learning with R: expert techniques for predictive modeling. Packt

publishing ltd.

Lei, B., Liu, Z., & Song, Y. (2021a). On stock volatility forecasting based on text mining and

deep learning under high frequency data. Journal of Forecasting, 40(8), 1596-1610. https://doi.org/10.1002/for.2794

Lei, B., Zhang, B., & Song, Y. (2021b). Volatility forecasting for high-frequency financial data

based on web search index and deep learning model. Mathematics, 9(4), 320. https://doi.org/10.3390/math9040320

Li, Y., Jiang, S., Li, X., & Wang, S. (2021). The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach. Energy Economics, 95, 105140. https://doi.org/10.1016/j.eneco.2021.105140

Liao, R., Yamaka, W., & Sriboonchitta, S. (2020). Exchange rate volatility forecasting by hybrid

neural network Markov switching beta-t-egarch. ieee Access, 8, 207563-207574. https://doi.org/10.1109/ACCESS.2020.3038564

Liu, Y. (2019). Novel volatility forecasting using deep learning–long short term memory recurrent neural networks. Expert Systems with Applications, 132, 99-109. https://doi.org/10.1016/j.eswa.2019.04.038

Liu, X., & Fu, H. (2016). Volatility forecasting for interbank offered rate using grey extreme

learning machine: The case of China. Chaos, Solitons & Fractals, 89, 249-254. https://doi.org/10.1016/j.chaos.2015.11.033

Liu, F. Y., & Liu, F. X. (2006). Currency options volatility forecasting with shift-invariant wavelet

transform and neural networks. In I. King, J. Wang, L. W. Chan, D. Wang (Eds.), Neural Information Processing. iconip 2006. Lecture Notes in Computer Science, vol 4234 (pp.461-468). Springer. https://doi.org/10.1007/11893295_51

Liu, S., Oosterlee, C. W., & Bohte, S. M. (2019). Pricing options and computing implied volatilities using neural networks. Risks, 7(1), 16. https://doi.org/10.3390/risks7010016

Medeiros, M. C., McAleer, M., Slottje, D., Ramos, V., & Rey-Maquieira, J. (2008). An alternative

approach to estimating demand: Neural network regression with conditional volatility for

high frequency air passenger arrivals. Journal of Econometrics, 147(2), 372-383. https://doi.org/10.1016/j.jeconom.2008.09.018

Mo, H., & Wang, J. (2013). Volatility degree forecasting of stock market by stochastic time strength neural network. Mathematical Problems in Engineering. https://doi.org/10.1155/2013/436795

Othman, A. H. A., Kassim, S., Rosman, R. B., & Redzuan, N. H. B. (2020). Prediction accuracy

improvement for Bitcoin market prices based on symmetric volatility information using

artificial neural network approach. Journal of Revenue and Pricing Management, 19, 314-330. https://doi.org/10.1057/s41272-020-00229-3

Ou, P., & Wang, H. (2012). Applications of Support Vector Machine in modeling and forecasting

stock market volatility. International Information Institute (Tokyo). Information, 15(8), 3365-3376.

Ou, P., & Wang, H. (2014). Volatility modelling and prediction by hybrid support vector regression with chaotic genetic algorithms. The International Arab Journal of Information

Technology, 11(3), 287-292. https://iajit.org/PDF/vol.11,no.3/4788.pdf

Patnaik, S. (2020). Applied machine learning and management of volatility, uncertainty, complexity & ambiguity (vuca). Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 9(2), 1409-1416. https://doi.org/10.3233/JIFS-179915

Peng, J., & Liu, Z. (2011). p th Moment stability of stochastic neural networks with Markov volatilities. Neural Computing and Applications, 20, 543-547. https://doi.org/10.1007/s00521-011-0542-5

Petneházi, G., & Gáll, J. (2019). Exploring the predictability of range‐based volatility estimators

using recurrent neural networks. Intelligent Systems in Accounting, Finance and Management, 26(3), 109-116. https://doi.org/10.1002/isaf.1455

Pradeepkumar, D., & Ravi, V. (2017). Forecasting financial time series volatility using particle

swarm optimization trained quantile regression neural network. Applied Soft Computing, 58, 35-52. https://doi.org/10.1016/j.asoc.2017.04.014

Qu, H., & Ji, P. (2014). Adaptive heterogeneous autoregressive models of realized volatility based on a genetic algorithm. Abstract and Applied Analysis. https://doi.org/10.1155/2014/943041

Qu, H., & Ji, P. (2016). Modeling realized volatility dynamics with a genetic algorithm. Journal

of Forecasting, 35(5), 434-444. https://doi.org/10.1002/for.2386

Petrozziello, A., Troiano, L., Serra, A., Jordanov, I., Storti, G., Tagliaferri, R., & La Rocca, M. (2022). Deep learning for volatility forecasting in asset management. Soft Computing, 26, 8553-8574. https://doi.org/10.1007/s00500-022-07161-1

Rahman, Q. A., Janmohamed, T., Pirbaglou, M., Clarke, H., Ritvo, P., Heffernan, J. M., & Katz,

J. (2018). Defining and predicting pain volatility in users of the Manage My Pain app: Analysis using data mining and machine learning methods. Journal of Medical Internet Research, 20(11), e12001. https://doi.org/10.2196/12001

Ramos-Pérez, E., Alonso-González, P. J., & Núñez-Velázquez, J. J. (2019). Forecasting volatility

with a stacked model based on a hybridized Artificial Neural Network. Expert Systems with Applications, 129, 1-9. https://doi.org/10.1016/j.eswa.2019.03.046

Santamaría-Bonfil, G., Frausto-Solís, J., & Vázquez-Rodarte, I. (2015). Volatility forecasting using support vector regression and a hybrid genetic algorithm. Computational Economics,

, 111-133. https://doi.org/10.1007/s10614-013-9411-x

Seo, M., & Kim, G. (2020). Hybrid forecasting models based on the neural networks for the volatility of Bitcoin. Applied Sciences, 10(14), 4768. https://doi.org/10.3390/app10144768

Sermpinis, G., Laws, J., & Dunis, C. L. (2013). Modelling and trading the realised volatility of the FTSE100 futures with higher order neural networks. The European Journal of Finance, 19(3), 165-179. https://doi.org/10.1080/1351847X.2011.606990

Slim, C. (2004, May). Forecasting the volatility of stock index returns: A stochastic neural network approach. In A. Laganá, M. L. Gavrilova, V. Kumar, Y. Mun, C. J. K. Tan, & O. Gervasi (Eds.), Computational Science and Its Applications – iccsa 2004. iccsa 2004. Lecture Notes in Computer Science, vol 3045 (pp. 935-944). Springer. https://doi.org/10.1007/978-3-540-24767-8_98

Stone, H. (2020). Calibrating rough volatility models: a convolutional neural network approach.

Quantitative Finance, 20(3), 379-392. https://doi.org/10.1080/14697688.2019.1654126

Tang, L. B., Tang, L. X., & Sheng, H. Y. (2009). Forecasting volatility based on wavelet support

vector machine. Expert Systems with Applications, 36(2-2), 2901-2909. https://doi.org/10.1016/j.eswa.2008.01.047

Tino, P., Schittenkopf, C., & Dorffner, G. (2001). Financial volatility trading using recurrent neural networks. ieee Transactions on Neural Networks, 12(4), 865-874. https://doi.org/10.1109/72.935096

Tseng, C. H., Cheng, S. T., Wang, Y. H., & Peng, J. T. (2008). Artificial neural network model

of the hybrid egarch volatility of the Taiwan stock index option prices. Physica A: Statistical Mechanics and its Applications, 387(13), 3192-3200. https://doi.org/10.1016/j.physa.2008.01.074

Tung, W. L., & Quek, C. (2011). Financial volatility trading using a self-organising neural-fuzzy

semantic network and option straddle-based approach. Expert Systems with Applications,

(5), 4668-4688. https://doi.org/10.1016/j.eswa.2010.07.116

Vortelinos, D. I. (2017). Forecasting realized volatility: har against Principal Components

Combining, neural networks and garch. Research in International Business and Finance, 39, 824-839. https://doi.org/10.1016/j.ribaf.2015.01.004

Vrontos, S. D., Galakis, J., & Vrontos, I. D. (2021). Implied volatility directional forecasting: a

machine learning approach. Quantitative Finance, 21(10), 1687-1706. https://doi.org/10.1080/14697688.2021.1905869

Wang, A., & Liu, Y. (2020). Intelligent financial management of company based on neural network and fuzzy volatility evaluation. Journal of Intelligent & Fuzzy Systems, 38(6), 7215-

https://doi.org/10.3233/JIFS-179798

Wang, B., Huang, H., & Wang, X. (2013). A support vector machine based msm model for financial short-term volatility forecasting. Neural Computing and Applications, 22, 21-28.

https://doi.org/10.1007/s00521-011-0742-z

Wang, C. P., Lin, S. H., Huang, H. H., & Wu, P. C. (2012). Using neural network for forecasting

txo price under different volatility models. Expert Systems with Applications, 39(5), 5025-

https://doi.org/10.1016/j.eswa.2011.11.038

Wang, F., Tang, S., & Li, M. (2021). Advantages of Combining Factorization Machine with Elman

Neural Network for Volatility Forecasting of Stock Market. Complexity, 1-12. https://doi.org/10.1155/2021/6641298

Wang, Y., Liu, H., Guo, Q., Xie, S., & Zhang, X. (2019). Stock volatility prediction by hybrid neural

network. ieee Access, 7, 154524-154534. https://doi.org/10.1109/ACCESS.2019.2949074

Weerasingha, J. P., Bandara, Y. M., & Edirisinghe, P. M. (2021). Determining the invoicing dates

for raw material order and finish product dispatch using neural networks under exchange rate volatility. International Journal of Logistics Research and Applications, 26(2), 211-231. https://doi.org/10.1080/13675567.2021.1945018

Wei, L. Y. (2012). An adaptive expectation genetic algorithm based on anfis and multinational

stock market volatility causality for taiex forecasting. Cybernetics and Systems, 43(5), 410-

https://doi.org/10.1080/01969722.2012.688687

Xia, A. G., Stroud, C. A., & Makar, P. A. (2011). Development of a simple unified volatility-based scheme (suvs) for secondary organic aerosol formation using genetic algorithms. Atmospheric Chemistry and Physics, 11(13), 6185-6205. https://doi.org/10.5194/acp-11-

-2011

Xu, L. (2021). Stock volatility prediction based on convolutional neural network. Basic & Clinical Pharmacology & Toxicology, 128(S1), 178.

Yang, R., Yu, L., Zhao, Y., Yu, H., Xu, G., Wu, Y., & Liu, Z. (2020). Big data analytics for financial

Market volatility forecast based on support vector machine. International Journal of Information Management, 50, 452-462. https://doi.org/10.1016/j.ijinfomgt.2019.05.027

Zeng, Y., & Klabjan, D. (2019). Online adaptive machine learning based algorithm for implied

volatility surface modeling. Knowledge-Based Systems, 163(1), 376-391. https://doi.org/10.1016/j.knosys.2018.08.039

Zhai, J., Cao, Y., & Liu, X. (2020). A neural network enhanced volatility component model. Quantitative Finance, 20(5), 783-797. https://doi.org/10.1080/14697688.2019.1711148

Zhu, E., Yang, G., & Liu, J. (2013). Comments and further improvements on “pth moment stability of stochastic neural networks with Markov volatilities.” Neural Computing and Applications, 23(3), 1179-1183. https://doi.org/10.1007/s00521-013-1396-9

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