[1]. Dixit, J., & Agrawal, V. (2019). Foresight for stock market volatility - a study in the Indian perspective. Foresight, ahead-of-print. https://doi.org/10.1108/FS-05-2019-0040

[2]. Plihal, T. (2021). Scheduled macroeconomic news announcements and Forex volatility forecasting. Journal of Forecasting, 40(8), 1379{1397. https://doi.org/10.1002/FOR.2773

[3]. Saad, S., Ahmad, N., & Maheran, M. J. (2017). Assessing stock market volatility for different sectors in Malaysia. Pertanika Journal of Science and Technology, 25, 631-648.

[4]. Chung, K. H., & Chuwonganant, C. (2018). Market volatility and stock returns: The role of liquidity providers. Journal of Financial Markets, 37, 17{34. https://doi.org/10.1016/J. FINMAR.2017.07.002

[5]. Zheng, Z., Qiao, Z., Takaishi, T., Stanley, H. E., & Li, B. (2014). Realized volatility and absolute return volatility: A comparison indicating market risk. PLOS ONE, 9(7), e102940. https://doi. org/10.1371/journal.pone.0102940

[6]. Huang, S., Liu, N., & Wang, Z. (2023). Empirical analysis of SSE 50 index volatility based on GARCH model. Fluctuation and Noise Letters, 24(4), 2440010. https://doi.org/10.1142/ S0219477524400108

[7]. Su, Z., Fang, T., & Yin, L. (2018). Does NVIX matter for market volatility? Evidence from Asia- Pacific markets. Physica A: Statistical Mechanics and Its Applications, 492, 506{516. https:// doi.org/10.1016/j.physa.2017.10.025

[8]. Gabriel, V. (2015). Sensitivity, persistence and asymmetric effects in international stock market volatility during the global financial crisis. Revista de Metodos Cuantitativos para la Economia y la Empresa, 16, 42{65.

[9]. Bhansali, V., & Harris, L. (2018). Everybody's doing it: Short volatility strategies and shadow financial insurers. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3071457

[10]. Naveen Kumar, P., & Minithra, R. (2021). Estimation of price volatility of Nifty 50 index using ADF and GARCH (1, 1). Indian Journal of Economics and Development, 17(2), 480-485. https://doi.org/10.35716/IJED/20286

[11]. Hewamana, R., Siriwardhane, D., & Rathnayake, A. (2022). Determinants of stock price volatility: A literature review. Sri Lanka Accounting Forum, 2(1), 44. doi:10.4038/sajf.v2i1.44

[12]. Damiran, S., Dorjdagva, O., Sukhee, B., & Myagmarsuren, T. (2022). Macroeconomic determinants of stock market volatility: Evidence from post-socialist countries. Journal of Eastern European and Central Asian Research, 9(4), 569-580. https://doi.org/10.15549/jeecar. v9i4.966

[13]. Ajao, M. G., Fredrick, E., & Robinson, F. (2022). Dividend policy determinants and stock price volatility in selected African stock markets. International Journal of Finance Research, 3(1), 27-48. https://doi.org/10.47747/ijfr. v3i1.659

[14]. Sutrisno, B. (2020). The determinants of stock price volatility in Indonesia. Economic Analysis Journal, 3(1), 73-79. https://doi. org/10.32493/eaj.v3i1.y2020.p73-79

[15]. Sun, Y., Liu, B., & Prodromou, T. (2021). The determinants of the COVID-19 related stock price overreaction and volatility. Studies in Economics and Finance. https://doi. org/10.1108/SEF-08-2021-0330

[16]. Van der Ploeg, A. P. C. (2004). Multifactor volatility models: Evidence from stock and option markets.

[17]. Ghosh, I., & Datta Chaudhuri, T. (2016). Understanding and forecasting stock market volatility through wavelet decomposition, statistical learning and econometric methods. SSRN Electronic Journal. https://doi. org/10.2139/ssrn.2930876

[18]. Situm, M. (2010). Time series volatility forecasting using linear regression and GARCH. SSRN Electronic Journal. https://doi. org/10.2139/ssrn.1549385

[19]. Popovici, O. C. (2015). A volatility analysis of the euro currency and the bond market. Economic Computation and Economic Cybernetics Studies and Research, 49(1), 67-79.

[20]. Amiram, D., Cserna, B., & Levy, A. (2016). Volatility, liquidity, and liquidity risk. SSRN Electronic Journal. https://doi.org/10.2139/ ssrn.2618424

[21]. Adam, K., Marcet, A., & Nicolini, J. P. (2008). Stock market volatility and learning. Research Papers in Economics.

[22]. De Lepper, M. R. (2016). Predictive analysis for financial volatility.

[23]. Smith, T. A., Caligiuri, A., & Montana, J. R. (2018). Using a multiple linear regression model to calculate stock market volatility. International Journal of Mathematics Trends and Technology, 57(4), 220-224. https://doi. org/10.14445/22315373/IJMTT-V57P531

[24]. Ou, C., Frausto-Solis, J., & Vazquez-Rodarte, I. (2015). Volatility forecasting using support vector regression and a hybrid genetic algorithm. Computing in Economics and Finance, 45(1), 111-133. https://doi.org/10.1007/S10614- 013-9411-X

[25]. Santamaria-Bonfil, G., Frausto-Solis, J., & Vazquez-Rodarte, I. (2015). Volatility forecasting using support vector regression and a hybrid genetic algorithm. Computing in Economics and Finance, 45(1), 111-133. https://doi. org/10.1007/S10614-013-9411-X

[26]. Rekabsaz, N., Lupu, M., Baklanov, A., DuÄr, A., Andersson, L., & Hanbury, A. (2017). Volatility prediction using financial disclosures sentiments with word embedding-based IR models. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 157-164. https://doi.org/10.18653/v1/P17- 1157

[27]. Douglas, K., Ziegelmann, F. A., & dos Santos, F. A. (2014). Volatility forecasting via MIDAS, HAR and their combination: An empirical comparative study for IBOVESPA. Journal of Forecasting, 33(3), 192-207. https://doi. org/10.1002/for.2287

[28]. Burtniak, I., & Suduk, N. (2022). Model 16 of determining stock market volatility. Visnyk Khmelnytskoho Natsionalnoho Universytetu, 302(1), 316-320. https://doi. org/10.31891/2307-5740-2022-302-1-53

[29]. Arevalo, J. L. S., de Souza, G. M., & Meurer, R. M. (2020). The Brazilian stock market indicator: Determinants to measure variation and direction. International Journal of Statistics and Management Systems, 3(5), 48-59. https:// doi.org/10.51386/25815946/IJSMS-V3I5P105

[30]. Mehmood, A., Ullah, M. H., & Sabeeh, N. U. (2019). Determinants of stock price volatility: Evidence from the cement industry. Accounting, 5(4), 145-152. https://doi.org/10.5267/j. ac.2019.02.002

[31]. Badshah, I. (2013). Quantile regression analysis of the asymmetric return-volatility relation. Journal of Futures Markets, 33(3), 235-265. https://doi.org/10.1002/fut.21551

[32]. Padmakumari, L., & Maheswaran, S. (2016). A regression-based approach to capturing the level dependence in the volatility of stock returns. Asian Economic and Financial Review, 6(12), 706-718. https://doi.org/10.18488/journal. aefr/2016.6.12/102.12.706.718

[33]. Sen, R. (2009). Functional data analysis for volatility. SSRN Electronic Journal.

[34]. Zhang, C. (2019). Continuous-time volatility regression in large panels. SSRN Electronic Journal. https://doi.org/10.2139/ ssrn.3385804

[35]. Makau, A. (2017). Multivariate regression analysis of oil price volatility on GDP growth in Kenya. American Journal of Theoretical and Applied Statistics, 6(1), 44. https://doi. org/10.11648/j.ajtas.20170601.16

[36]. Josheski, D., & Lazarov, D. (2012). Exchange rate volatility and trade: A meta-regression analysis. SSRN Electronic Journal. https:// doi.org/10.2139/ssrn.2077678

[37]. Allen, D. E., Singh, A. K., Powell, R., McAleer, M., Taylor, J. E., & Thomas, L. C. (2012). The volatility-return relationship: Insights from linear and non-linear quantile regressions. Research Papers in Economics.

[38]. Waweru, N. N. (2013). Analysis of the determinants of stock price volatility at Nairobi Securities Exchange.

[39]. Parkinson, M. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. The Journal of Business, 53(1), 61-65. http://www.jstor.org/stable/2352357

[40]. Jolliffe, I. (2013). Principal component analysis. Springer New York.

[41]. Hastie, T., Friedman, J., & Tibshirani, R. (2001). The elements of statistical learning. Springer. https://doi.org/10.1007/978-0-387-21606-5

[42]. Sakamoto, Y., et al. (2024). Applications of the elbow method in K-means clustering.

[43]. Umargono, E., Suseno, J., & Gunawan, S. K. (2020). K-means clustering optimization using the elbow method and early centroid determination based on mean and median formula. Proceedings of the International Conference on Engineering, Technology and Industrial Application, 19-26. https://doi. org/10.2991/assehr.k.201010.019

[44]. Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65.

[45]. Lenssen, L., & Schubert, E. (2024). Medoid silhouette clustering with automatic cluster number selection. Information Systems, 120, 102290.

[46]. Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2), 224-227.

[47]. Wijaya, Y. A., et al. (2021). Cluster validation techniques in data analysis using Davies- Bouldin Index. Proceedings of the International Conference on Industrial Technology and Application, 101-109.

[48]. Sarikovanlik, V., Koy, A., Akkaya, M., Yildirim, H.H., & Kantar, L. (2019). Finans Biliminde Ekonometri Uygulamalari, Seckin Yayincilik, Ankara.

[49]. Breusch, T.S., & Pagan, A. R. (1980). The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics. The Review of Economic Studies, 239-253. https:// doi.org/10.2307/2297111

[50]. Peseran, M. H. H. M. (2004). General Diagnostic Test for Cross Section Dependence in Panels. Working Paper, University of Cambridge, Cambridge, United Kingdom.

[51]. Harris, R. D. F., & Tzavalis, E. (1999). Inference for Unit Roots in Dynamic Panels Where The Time Dimension is Fixed, Journal of Econometrics, 91, 201-226. https://doi. org/10.1016/S0304-4076(98)00076-1

[52]. Hausman, J. A. (1978). Specification Test in Econometrics, Econometrica, 46(6), 1251-1271.

[53]. Tatog¸lu, F. Y. (2012). Panel Veri Ekonometrisi, Beta Yayincilik, Istanbul.

[54]. Akin, I., & Akin, M. (2024). Behavioral finance impacts on US stock market volatility: An analysis of market anomalies. Behavioural Public Policy, 1-25. https://doi.org/10.1017/bpp.2024.13

[55]. Dichev, I. D., & Zheng, X. (2024). The volatility of stock investor returns. Journal of Financial Markets, 70, 100927. https://doi.org/10.1016/j. finmar.2024.100927

[56]. Nur, T., Ege, I_., & Topalog¸lu, E. E. (2023). The moderator role of managerial overconfidence in the relationship between r&d volatility and firm value: An application in borsa istanbul. Akademik Arastirmalar ve Calismalar Dergisi (AKAD), 15(28), 1-11. https://doi.org/10.20990/ kilisiibfakademik.1232263

[57]. Gahlot, P., Sachdeva, K., Agnihotri, S., & Giri, J. N. (2024). Influence of behavioral biases on investor decision-making in delhi-ncr. Icinde R. Sharma & K. Mehta (Ed.), Deep Learning Tools for Predicting Stock Market Movements (1. bs, ss. 363-389). Wiley. https://doi.org/10.1002/9781394214334.ch15

[58]. Takahashi, H. (2013). Analyzing the influence of Value at Risk on financial markets through agent-based modeling. International Journal of Knowledge-based and Intelligent Engineering Systems, 17(4), 257-266. https://doi.org/10.3233/KES-130276

[59]. Bulut, E. (2024). Sectoral volatility in borsa istanbul: A garch-based comparative analysis. Ahi Evran UÄniversitesi Sosyal Bilimler EnstituÄsuÄ Dergisi, 10(2), 507-522. https://doi. org/10.31592/aeusbed.1355079

[60]. Ikizlerli, D. (2022). The relation between trading volume and return volatility: Evidence from borsa istanbul. Business and Economics Research Journal, 4. https://doi.org/10.20409/ berj.2022.392

[61]. Kaya, A., & Yarbasi, I_. Y. (2021). Forecasting of volatility in stock exchange markets by ms-garch approach: An application of borsa istanbul. Ekonomi, Politika & Finans Arastirmalari Dergisi. https://doi.org/10.30784/ epfad.740815