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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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.
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.

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