Volatility Analysis and Forecasting of Selected DSE Stocks Using GARCH Models: A Case Study of SPL, IFICB, and KBPWBI

Authors

  • Md. Jakaria Hossen Shikder Department of Science and Humanities, Military Institute of Science and Technology (MIST), Mirpur-12, Dhaka-1216, Bangladesh
  • Mustafa Saadman Sakib Department of Economics, Southeast University, Dhaka, Bangladesh
  • Khondaker Fahad Mia Department of Computer Science & Engineering, National Institute of Textile Engineering (NITER), Nayarhat, Savar, Dhaka, Bangladesh
  • M. Osman Gani Department of Mathematics, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh
  • M. M. Rahman Department of Mathematics, Bangladesh University of Engineering and Technology (BUET), Dhaka-1000, Bangladesh
  • Md Ikramul Haque Department of Science and Humanities, Military Institute of Science and Technology (MIST), Mirpur-12, Dhaka-1216, Bangladesh

DOI:

https://doi.org/10.18034/ajtp.v12i1.760

Keywords:

Volatility, Stock Price, DSE, Return, IFICB, KBPWBI, SPL

Abstract

This research aims to estimate future volatility patterns of specific companies by evaluating the volatility dynamics of the Dhaka Stock Exchange (DSE) and using GARCH (Generalized Autoregressive Conditional Heteroscedasticity) models in conjunction with other statistical and mathematical methodologies. The research examines daily closing prices from January 2018 to June 2023 for Khan Brothers PP Woven Bag Industries Limited (KBPWBI), IFIC Bank Limited (IFICB), and Square Pharmaceuticals Limited (SPL). Empirical findings reveal non-stationary behavior in the time series distribution of daily returns, exhibiting both positive and negative skewness. High volatility and sharp fluctuations were particularly prominent in IFICB and KBPWBI compared to SPL. The computed standard deviation highlights KBPWBI as the most volatile stock, registering significant price swings with volatility peaking at 4.23% in 2020 and 3.86% in 2021. Conversely, SPL demonstrated a gradual decline in volatility over the past three years, indicating increasing market stability for this firm. Utilizing the EGARCH (Exponential Generalized Autoregressive Conditional Heteroskedasticity) model, the study further explores the return volatility of these three IPOs, projecting volatility trends from June 2023 through December 2025. The results reveal evidence of volatility clustering and asymmetric effects across all stocks. IFICB exhibited the highest volatility levels, whereas SPL maintained the lowest, with a projected volatility spike anticipated for SPL in late 2025. Notably, mid-2024 is forecasted to be the most stable period for all three IPOs, marking a temporary phase of reduced market turbulence.

Downloads

Download data is not yet available.

References

Abdullah, S. M., Siddiqua, S., Siddiquee, M. S. H., & Hossain, N. (2017). Modelling and forecasting exchange rate volatility in Bangladesh using GARCH models: A comparison based on normal and student’s t-error distribution. Financial Innovation, 3(18), 1–19.

Abken, P. A., & Nandi, S. (1996). Options & volatility. Economic Review, 18, 21–35. https://www.researchgate.net/publication/5025568

Ariff, M., & Finn, F. J. (1989). Announcement effects and market efficiency in a thin market: An empirical application to the Singapore equity market. Asia Pacific Journal of Management, 6, 243–265. https://doi.org/10.1007/BF01733767

Aziz, M. S. I., & Uddin, M. N. (2014). Volatility estimation in the Dhaka Stock Exchange (DSE) returns by GARCH models. Asian Business Review, 4(1), 41–50. https://doi.org/10.18034/abr.v4i1.72

Babikir, A., Gupta, R., Mwabutwa, C., & Owusu-Sekyere, E. (2012). Structural breaks and GARCH models of stock return volatility: The case of South Africa. Economic Modelling, 29(6), 2435–2443. https://doi.org/10.1016/j.econmod.2012.06.038

Basher, S. A., Hassan, M. K., & Islam, A. M. (2007). Time-varying volatility and equity returns in Bangladesh stock market. Applied Financial Economics, 17(17), 1393–1407. https://doi.org/10.1080/09603100600771034

Chowdhury, A. (2014). Micro and macro dynamics of the stock market: An investigation into the Dhaka Stock Exchange (DSE) [PhD thesis, University of York]. White Rose eTheses. https://etheses.whiterose.ac.uk/id/eprint/8573/

Chowdhury, A., Uddin, M., & Anderson, K. (2013). Investors’ behaviour and weekend effect: Evidence from an emerging market. 1st Paris Financial Management Conference.

Hafer, R. W., & Hein, S. E. (2007). The stock market. Greenwood Press. https://www.amazon.com/dp/0313338248

Hasan, M. A., & Zaman, A. (2017). Volatility nexus between stock market and macroeconomic variables in Bangladesh: An extended GARCH approach. Scientific Annals of Economics and Business, 64(2), 233–243. https://doi.org/10.1515/saeb-2017-0015

Hossain, M. S., Baten, M. A., & Mukta, F. B. (2021). Forecasting volatility of selected banks of Dhaka Stock Exchange (DSE), Bangladesh with GARCH (p, q) type models. Journal of Economic Cooperation and Development, 42(1), 117–142. https://www.researchgate.net/publication/354967910

Islam, A., & Khaled, M. (2005). Tests of weak-form efficiency of the Dhaka Stock Exchange. Journal of Business Finance & Accounting, 32(7–8), 1613–1624. https://doi.org/10.1111/j.0306-686X.2005.00642.x

Karmakar, M. (2005). Modelling conditional volatility of the Indian stock markets. Vikalpa, 30(3), 21–38. https://doi.org/10.1177/0256090920050303

Mala, R., & Reddy, M. (2007). Measuring stock market volatility in an emerging economy. International Research Journal of Finance and Economics, 8, 126–133.

Mia, M., & Rahman, A. (2016). Modelling volatility of daily stock returns: Is GARCH(1,1) enough? American Scientific Research Journal for Engineering, Technology, and Sciences, 18(1), 29–39. https://www.researchgate.net/publication/335749475

Mollik, A. T. (2009). Risk-return trade-off in emerging markets: Evidence from Dhaka Stock Exchange, Bangladesh. 22nd Australasian Finance and Banking Conference.

Naik, N., & Mohan, B. R. (2021). Stock price volatility estimation using regime switching technique—Empirical study on the Indian stock market. Mathematics, 9(14), 1595. https://doi.org/10.3390/math9141595

Oseni, I. O., & Nwosa, P. I. (2011). Stock market volatility and macroeconomic variables volatility in Nigeria: An exponential GARCH approach. European Journal of Business and Management, 3(12), 43–53. https://www.iiste.org/Journals/index.php/EJBM/article/view/721

Siddique, M. N., & Begum, N. N. (2016). Volatility of Dhaka Stock Exchange. International Journal of Economics and Finance, 8(5). https://doi.org/10.5539/ijef.v8n5p

Xing, D. Z., Li, H. F., Li, J. C., & Long, C. (2021). Forecasting price of financial market crash via a new nonlinear potential GARCH model. Physica A: Statistical Mechanics and its Applications, 566, 125649. https://doi.org/10.1016/j.physa.2020.125649

Downloads

Published

2025-06-15

Issue

Vol. 12 No. 1 (2025): January-December Issue

Section

Research Articles

License

Copyright (c) 2025 American Observer Press

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Articles can be read and shared for noncommercial purposes under the following conditions:

  • BY: Attribution must be given to the original source (Attribution)
  • NC: Works may not be used for commercial purposes (Noncommercial)

How to Cite

Shikder, M. J. H., Sakib, M. S., Mia, K. F., Gani, M. O., Rahman, M. M., & Haque, M. I. (2025). Volatility Analysis and Forecasting of Selected DSE Stocks Using GARCH Models: A Case Study of SPL, IFICB, and KBPWBI. American Journal of Trade and Policy, 12(1), 13-22. https://doi.org/10.18034/ajtp.v12i1.760