DOI : https://doi.org/10.18517/ijaseit.14.4.19776

Analyzing Composite Stock Price Index Volatility in Response to Changes in Data Structure Using Bayesian Markov-Switching GARCH

Ermanely (1), Dodi Devianto (2), Ferra Yanuar (3), Rahmat Hidayat (4)
(1) Department of Mathematics and Data Science, Universitas Andalas, Padang, Indonesia
(2) Department of Mathematics and Data Science, Universitas Andalas, Padang, Indonesia
(3) Department of Mathematics and Data Science, Universitas Andalas, Padang, Indonesia
(4) Department of Information Technology, Politeknik Negeri Padang, Padang, Indonesia
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How to cite (IJASEIT) :
Ermanely, et al. “Analyzing Composite Stock Price Index Volatility in Response to Changes in Data Structure Using Bayesian Markov-Switching GARCH”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 4, Aug. 2024, pp. 1209-15, doi:10.18517/ijaseit.14.4.19776.
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Rapid economic growth was observed to have motivated economic actors and investors to maximize business performance using different methods. An example of these are stocks or shares securities showing that a person or entity has a stake in a company. Investors are primarily drawn to stocks due to their potential for substantial profits closely tied to the volatility in the stock market. The prevalence of this volatility has long been addressed using the ARCH/GARCH model, but it is not ideal for datasets experiencing structural changes. Therefore, a model was developed using the Bayesian Markov-switching GARCH approach to effectively capture the heteroscedastic component and structural changes in data and mitigate certain limitations, especially those associated with a small sample size. This study adopted the composite stock price index (CSI) data from March 2020 to April 2021 to model and understand the volatility. The results showed that the Bayesian Markov-switching GARCH model with the most negligible variance provided the best fit. It was also discovered that a more minor error variance corresponded to lower data volatility. Moreover, the concept of value at risk was used to assess the investment risk based on the criterion that a decrease in the CSI investments led to a reduction in the level of risk faced by the investors.

J.D. Hamilton and R. Susmel, “Autoregressive conditional heteroscedasticity and changes in regime,” Journal of Econometrics, vol. 64, no. 1-2, pp. 307-333, 1994, doi: 10.1016/0304-4076(94)90067-1.

S.F. Gray, “Modeling the conditional distribution of interest rates as a regime-switching process,” Journal of Financial Economics, vol. 42, no. 1, pp. 27-62, 1996, doi: 10.1016/0304-405X(96)00875-6.

M. Cavicchioli, “Spectral analysis of Markov switching GARCH models with statistical inference Scandinavian,“ Journal of Statistics. vol. 50, no. 1, pp. 102-119, 2023, doi: 10.1111/sjos.12571.

D.C.H. Wee, F. Chen, and W.T.M. Dunsmuir, “Likelihood inference for Markov-switching GARCH(1,1) models using sequential Monte Carlo,” Econometrics and Statistics, vol. 21, pp. 50-68, 2022, doi: 10.1016/j.ecosta.2020.03.004.

D. Devianto, M. Maiyastri, and D.R. Fadhilla, “Time series modeling for risk of stock price with value at risk computation,” Applied Mathematical Sciences, vol. 9, no. 56, pp. 2779-2787, 2015, doi: 10.12988/ams.2015.52144.

D. Devianto, M. Yolanda, M. Maiyastri, and F. Yanuar, “The soft computing FFNN method for adjusting heteroscedasticity on the time series model of currency exchange rate,” Applied Mathematical Sciences, vol. 9, pp.1–18, 2023, doi: 10.3389/fams.2023.1045218.

Y. Runfang, D. Jiangze, and L. Xiaotao, “Improved forecast ability of oil market volatility based on combined Markov switching and GARCH-class model,” Procedia Computer Science, vol. 122, pp. 415–422, 2017, doi: 10.1016/j.procs.2017.11.388.

L.D. Persio and S. Vettori, “Markov switching model analysis of implied volatility for market indexes with applications to S&P 500 and DAX,” Journal of Mathematics, vol. 2014, pp. 1-17, 2014, doi: 10.1155/2014/753852.

R.C.K. Burdekin and R Tao, “Chinese influences on inflation determination in Australia and the ASEAN group: A Markov-switching analysis,” Asia and the Global Economy, vol. 2, no. 2, pp. 1-17, 2022, doi: 10.1016/j.aglobe.2022.100037

O.V.D. Torre-Torres, E. Galeana-Figueroa, and J. Alvarez-Garcia, “A Test of Using Markov-Switching GARCH Models in Oil and Natural Gas Trading,” Energies, vol. 13, no. 129, pp. 1-24, 2020, doi: 10.3390/en13010129.

M. Shakeel, M.R. Rabbani, I.T. Hawaldar, V. Chhabra, and F.K. Zaidi, “Is there an intraday volatility spillover between exchange rate, gold and crude oil?,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 9, no. 3, pp. 100094, 2023, doi: 10.1016/j.joitmc.2023.100094.

D. Ardia, K. Bluteau, and M. Ruede, “Regime changes in Bitcoin GARCH volatility dynamics,” Finance Research Letters, vol. 29, pp. 266–271, 2019, doi: 10.1016/j.frl.2018.08.009.

C-Y. Tan, Y-B. Koh, K-Haur Ng, and K-Huat Ng, “Dynamic volatility modelling of Bitcoin using time-varying transition probability Markov-switching GARCH model,” The North American Journal of Economics and Finance, vol. 56, pp. 101377, 2021, doi: 10.1016/j.najef.2021.101377.

G.M. Caporale and T. Zekokh, “Modeling volatility of cryptocurrencies using Markov-switching GARCH models,” Research in International Business and Finance, vol. 48, pp. 143–155, 2019, doi:10.1016/j.ribaf.2018.12.009.

C. Wu, “Window effect with Markov-switching GARCH model in the cryptocurrency market,” Chaos, Solitons and Fractals, vol. 146, pp. 110902, 2021, doi: 10.1016/j.chaos.2021.110902.

I. Barjasic and N. Antulov-Fantulin, “Time-Varying Volatility in Bitcoin Market and Information Flow at Minute-Level Frequency,” Frontiers in Physics, vol. 9, pp. 1-12, 2021, doi: 10.3389/fphy.2021.644102.

J. Wang, F. Ma, C. Liang, and Z. Chen, “Volatility forecasting revisited using Markov-switching with time-varying probability transition,” International Journal of Finance and Economics, vol. 27, no.1, pp. 1387-1400, 2019, doi: 10.1002/ijfe.2221.

Z. Pan, Y. Wang, C. Wu, and L. Yin, “Oil price volatility and macroeconomic fundamentals: A regime-switching GARCH-MIDAS model,” Journal of Emperical Finance, vol. 43, pp. 130-142, 2017, doi: 10.1016/j.jempfin.2017.06.005.

L. Wang, J. Wu, Y. Cao, and Y. Hong, “Forecasting renewable energy stock volatility using short and long-term Markov switching GARCH-MIDAS models: Either, neither or both?,” Energy Economics, vol. 111, pp. 106056, 2022, doi: 10.1016/j.eneco.2022.106056.

M. Segnon, R. Gupta, and B. Wilfling, “Forecasting stock market volatility with regime-switching GARCH-MIDAS: The role of geopolitical risks, International Journal of Forecasting, vol. 40, no. 1, pp. 29-43, 2024, doi: 10.1016/j.ijforecast.2022.11.007.

C.B. Kim, “Leverage effect of HRCI volatility and the volatility impact on Korean export container volume before and after the global financial crisis: Application of ARIMA-EGARCH and GIRF,” .The Asian Journal of Shipping and Logistics, vol. 34, no. 3, pp. 231–237, 2018, doi: 10.1016/j.ajsl.2018.09.006.

R. Casarin, M. Costantini, and A. Osuntuyi, “Bayesian nonparametric panel Markov-switching GARCH Models,” Journal of Business and Economic Statistics, vol. 14 Feb 2023, pp. 1-12, 2023, doi: 10.1080/07350015.2023.2166049.

Y.A. Shiferaw, “The Bayesian MS-GARCH Model and Value at Risk in South African Agricultural Commodity Price Markets. International Association of Agricultural Economists, vol. 275991, pp. 1-15, 2018, doi: 10.22004/ag.econ.275991.

W. Nyakabawo, R. Gupta, and H.A. Marfatia, “ High frequency impact of monetary policy and macroeconomic surprises on us MSAs, aggregate US housing returns and asymmetric volatility, Advances in Decision Sciences, vol. 22(A), pp. 204-229, 2018, doi: 10.47654/v22y2018i1p204-229.

D. Devianto, K. Ramadani, M. Maiyastri, Y. Asdi, and M. Yolanda, “The hybrid model of autoregressive integrated moving average and fuzzy time series Markov chain on long-memory data,” Applied Mathematical Sciences, vol. 8, pp.1–15, 2022, doi: 10.3389/fams.2022.1045241.

S.D. Sanzo, “A Markov switching long memory model of crude oil price return volatility,” Energy Economics, vol. 74, pp. 351-359, 2018, doi: 10.1016/j.eneco.2018.06.015.

H. Xiao, Q. Zhu, and H.R. Karimi, “Stability analysis of semi-Markov switching stochastic mode-dependent delay systems with unstable subsystems,” Chaos, Solitons and Fractals, vol. 165, pp. 112791, 2022, doi: 10.1016/j.chaos.2022.112791.

J. Liu, Y. Pan, J. Pan, and A. Almarashi. Markov-switching Poisson generalized autoregressive conditional heteroscedastic models,” Statistics and Its Interface, vol. 16, no. 4, pp. 531 – 544, 2023, doi: 10.4310/22-SII741

L. Feng, T. Fu,Y. Shi, and Z. Wang, “Discussions on the zero-drift GARCH model: Evidence from an Markov regime-switching extension,” Finance Research Letters, vol. 40, pp. 101713, 2021, doi: 10.1016/j.frl.2020.101713.

D. Ardia, K.B. Bluteau, K. Boudt, L. Catania, and D-A. Trottier, “Markov-Switching GARCH Models in R: The MSGARCH Package,” Journal of Statistical Software, vol. 91, no. 4, pp. 1-38, 2019, doi: 10.18637/jss.v091.i04.

D. Devianto, “On the class of infinitely divisible exponential distribution,” Journal of Physics: Conference Series, vol. 1097. IOP Publishing, pp. 012084, 2018, doi: 10.1088/1742-6596/1097/1/012084.

D. Devianto, Maiyastri, and K. Azmi, “On the Levy measure property of gamma distribution,” AIP Conference Proceedings, vol. 2242, pp. 030003, 2020, doi: 10.1063/5.0007842.

F. Goncalves, N. Mendes-Lopes, and F. Silva, “Infinitely divisible distributions in integer-valued GARCH models,” Journal of Time Series Analysis, vol. 36, no. 64, pp. 503-527, 2015, doi: 10.1111/jtsa.12112.

F. Zhu, M.L. Bianchi, Y.S.A. Kim, F. Fabozzi, and H. Wu “Learning for infinitely divisible GARCH models in option pricing,” Studies in Nonlinear Dynamics and Econometrics,vol. 25, no. 3, pp. 35-62, 2021, doi: 10.1515/snde-2019-0088.

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