DOI : https://doi.org/10.18517/ijaseit.15.3.12650

Statistical Investigation of Volatility Clustering and Shock Behavior Using Conditional Models

Husam Abdulrazzak Rasheed (1), Haifa Taha Abd (2), Nazik J. Sadik (3)
(1) Department of Statistics, College of Administration and Economics, Mustansiriyah University, Iraq
(2) Department of Statistics, College of Administration and Economics, Mustansiriyah University, Iraq
(3) Department of Statistics, College of Administration and Economics, Baghdad University, Iraq
Fulltext View | Download
How to cite (IJASEIT) :
[1]
H. A. Rasheed, H. Taha Abd, and N. J. Sadik, “Statistical Investigation of Volatility Clustering and Shock Behavior Using Conditional Models”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 3, pp. 839–847, Jun. 2025.
Citation Format :
Evaluating businesses and initiatives using the stock market is crucial. Its success or failure is appraised, thereby helping to raise knowledge and comprehension of the realities of companies and projects among investors. Falling stock prices prompt management or policy adjustments aimed at improving their condition. Accordingly, the New York Stock Exchange, the Shanghai Stock Exchange, and the Hong Kong Stock Exchange—three of the most crucial financial hubs in the world—were examined. These exchanges provide a platform for trading equities and exchange-traded funds, thereby enabling investors to have a range of investment options. This study is to investigate the changes and shocks in the stock prices of the New York Stock Exchange, the Shanghai Stock Exchange, and the Hong Kong Stock Exchange. We employed the conditional models GARCH, PARCH, TGARCH, and EGARCH. From 2000 to early 2005, these models drew on weekly historical data for every stock market. The findings show that the conditional models successfully identified stock price shocks and changes. They might also examine these shocks and changes to determine the most significant economic, social, and political ones that had a substantial impact on their performance. This study contributes to the understanding of shocks and volatility in stock values, which can inform wise investment choices.

M. Abadi et al., "TensorFlow White Papers - Large-Scale Machine Learning on Heterogeneous Distributed Systems," 2019. [Online]. Available: https://www.tensorflow.org/about/bib.

H. T. Abd, H. A. Rasheed, and A. A. Rasheed, "An examination of the impulse response function under configuration of the multiple autoregressive," Ind. Eng. Manag. Syst., vol. 23, no. 3, pp. 300–310, 2024, doi: 10.7232/iems.2024.23.3.300.

F. Aliyev, R. Ajayi, and N. Gasim, "Modelling asymmetric market volatility with univariate GARCH models: Evidence from Nasdaq-100," J. Econ. Asymmetries, vol. 22, p. e00167, Nov. 2020, doi:10.1016/j.jeca.2020.e00167.

I. Berkes and L. Horváth, "The efficiency of the estimators of the parameters in GARCH processes," Ann. Stat., vol. 32, no. 2, pp. 633–655, 2004, doi: 10.1214/009053604000000120.

T. Bollerslev, "Generalized autoregressive conditional heteroskedasticity," J. Econom., vol. 31, no. 3, pp. 307–327, 1986, doi:10.1016/0304-4076(86)90063-1.

Z. Ding, C. W. J. Granger, and R. F. Engle, "A long memory property of stock market returns and a new model," J. Empir. Finance, vol. 1, no. 1, pp. 83–106, 1993, doi: 10.1016/0927-5398(93)90006-D.

R. Engle, "New frontiers for ARCH models," J. Appl. Econom., vol. 17, no. 5, pp. 425–446, 2002, doi: 10.1002/jae.683.

F. Pérez-Hernández, A. Arévalo-de-Pablos, and M. Camacho-Miñano, "A hybrid model integrating artificial neural network with multiple GARCH type models and EWMA for performing the optimal volatility forecasting of market risk factors," Expert Syst. Appl., vol. 243, p. 122896, Jun. 2024, doi: 10.1016/j.eswa.2023.122896.

W. Hongwiengjan and D. Thongtha, "An analytical approximation of option prices via TGARCH model," Econ. Res.-Ekon. Istraž., vol. 34, no. 1, pp. 948–969, 2021, doi: 10.1080/1331677X.2020.1805636.

D. B. Nelson, "Conditional heteroskedasticity in asset returns: A new approach," Econometrica, vol. 59, no. 2, pp. 347–370, 1991, doi:10.2307/2938260.

W. G. Schwert, "Why does stock market volatility change over time?," J. Finance, vol. 44, no. 5, pp. 1115–1153, 1989, doi:10.1111/j.1540-6261.1989.tb02647.x.

C. H. Tseng, S. T. Cheng, Y. H. Wang, and J. T. Peng, "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A, vol. 387, no. 13, pp. 3192–3200, 2008, doi: 10.1016/j.physa.2008.01.074.

L. T. Mouhmmd et al., "The effect of firm type on the relationship between accounting quality and trade credit in listed firms," Corp. Bus. Strategy Rev., vol. 4, no. 2, pp. 175–183, 2023, doi:10.22495/cbsrv4i2art16.

I. O. B. Al-Fahad, H. kadhim Sharaf, L. N. Bachache, and N. K. Bachache, “Identifying the mechanism of the fatigue behavior of the composite shaft subjected to variable load,” East.-Eur. J. Enterp. Technol., vol. 3, no. 7 (123), pp. 37–44, Jun. 2023, doi:10.15587/1729-4061.2023.283078.

R. F. Ghazi, J. S. Chiad, and F. M. Abdulghani, "Design and manufacturing a smart shoe for diabetic foot ulcer monitoring and prediction system using internet-of-things technology," J. Braz. Soc. Mech. Sci. Eng., vol. 46, no. 2, Jan. 2024, doi: 10.1007/s40430-023-04591-2.

F. T. Al-Maliky and J. S. Chiad, "Study and analysis the flexion moment in active and passive knee prosthesis using back propagation neural network predictive," J. Braz. Soc. Mech. Sci. Eng., vol. 44, no. 11, Oct. 2022, doi: 10.1007/s40430-022-03850-y.

M. M. Yahya, A. M. Al-Mushehdany, and H. J. M. Alalkawi, "Evaluation of buckling of 2024-T3 under high temperatures," Int. J. Heat Technol., vol. 40, no. 4, pp. 947–952, 2022, doi:10.18280/ijht.400411.

A. M. Al-Mushehdany et al., "Using modern concepts in the design of extrusion dies to improve the mechanical extrusion and fatigue properties for AA1100," East.-Eur. J. Enterp. Technol., vol. 5, no. 12, pp. 31–37, Oct. 2022, doi: 10.15587/1729-4061.2022.265712.

R. M. Abed et al., "Numerical analysis of the aluminium alloy structure of the brackets based on conceptualization processes," J. Adv. Res. Appl. Mech., vol. 124, no. 1, pp. 163–171, 2024, doi:10.37934/aram.124.1.163171.

J.-M. Zakoian, "Threshold heteroskedastic models," J. Econ. Dyn. Control, vol. 18, no. 5, pp. 931–955, 1994, doi: 10.1016/0165-1889(94)90039-6.

Ulfa et al., "Feasibility study of grid connected solar PV design with battery at Sandai 150/20 kV substation as a Tayap 4 feeder backup when system blackout occurs," Int. J. Comput. Eng., vol. 1, no. 4, pp. 119–125, 2024, doi: 10.62527/comien.1.4.28.

S. Kehinde et al., "A commentary on integrating green technology into civil engineering: Innovative approaches for sustainable infrastructure development in urban areas," Int. J. Comput. Eng., vol. 1, no. 4, pp. 126–128, 2024, doi: 10.62527/comien.1.4.29.

H. Santoso, G. Ferreira Soares, and C. M. Angelo, "Detection of children's facial expressions on the effects of playing games using CNN algorithm," Int. J. Adv. Sci. Comput. Eng., vol. 6, no. 3, pp. 152–157, 2024.

N. Suico et al., "Vouching the digital literacy in instruction viz-a-viz performance: Contextualized enhancement activities," Int. J. Adv. Sci. Comput. Eng., vol. 6, no. 3, pp. 138–151, 2024.

I. O. B. Al-Fahad, A. D. Hassan, B. M. Faisal, and H. kadhim Sharaf, “Identification of regularities in the behavior of a glass fiber-reinforced polyester composite of the impact test based on ASTM D256 standard,” East.-Eur. J. Enterp. Technol., vol. 4, no. 7 (124), pp. 63–71, Aug. 2023, doi: 10.15587/1729-4061.2023.286541.

A. Kadhim and J. Y. Mustafa, "User-friendly interface attendance system based on Python libraries and deep learning," Int. J. Data Sci., vol. 5, no. 2, pp. 80–87, Dec. 2024, doi: 10.18517/ijods.5.1.56-62.2024.

D. M. Abdulhadi and H. A. Rasheed, "Using opposition golden jackal optimization algorithm (OGJO) in improving some kernel semiparametric models: A comparative study," Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 2, pp. 478–484, Apr. 2025, doi:10.18517/ijaseit.15.2.12808.

B. M. Lafta and A. A. Rasheed, "Comparing some adjacency matrices to estimate a spatial negative binomial regression model," Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 1, pp. 189–195, Feb. 2025, doi:10.18517/ijaseit.15.1.12554.

H. M. Kadhim et al., "Mechanical investigation on the foot orthosis of carbon fiber (CF) based on the dynamic loads: A finite element analysis," Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 2, pp. 610–616, Apr. 2025, doi: 10.18517/ijaseit.15.2.12616.

A. K. Hamzah et al., "Fatigue life estimation under high temperature and variable loading of AA7001-T6 using shot peening," Tikrit J. Eng. Sci., vol. 31, no. 2, pp. 168–174, 2024, doi: 10.25130/tjes.31.2.16.

N. K. Al-Hayali et al., "Analysis and evaluation of a quasi-passive lower limb exoskeleton for gait rehabilitation," Al-Khwarizmi Eng. J., vol. 17, no. 4, pp. 36–47, 2021, doi: 10.22153/kej.2021.12.007.

Creative Commons License

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

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).