Analysis of the effect of Regional Stock Market on the Jakarta Composite Index using Markov Regime Switching Regression

https://doi.org/10.35912/jomabs.v1i4.2372
Published: Aug 22, 2024

Abstract:

Purpose: In the current era of information, global stock market interconnections significantly influence investment decisions. Changes in one market rapidly affect others. The co-movement of stock markets presents challenges and opportunities for investors, whereas volatility spillovers complicate risk management and investment strategies.

Research Methodology: This study examines the influence of the Nikkei 225, Straits Times Index, and Shanghai Composite Index on the Jakarta Composite Index across the pre-pandemic, pandemic, and post-pandemic phases.

Results: Utilizing the hidden Markov model with regime-switching regression, this study identifies changes in market behavior due to economic shifts during the pandemic, revealing two regimes: synchronization and desynchronization.

Limitations: Pre-COVID-19, the Jakarta Composite Index shows strong synchronization with the Nikkei 225 and Straits Times Index, while the Shanghai Composite Index has an insignificant impact. During the COVID-19 pandemic, frequent desynchronization occurred due to high uncertainty and volatility, with only the Straits Times Index significantly influencing the Jakarta Composite Index. Post-pandemic, synchronization between the JCI and regional markets strengthened again. This study highlights the consistent influence of the Nikkei 225 and Straits Times Index, while the Shanghai Composite Index remains insignificant.

Contributions: This study contributes significantly to the understanding of regional stock market relationships and offers valuable insights for academia and practice.

Keywords:
1. Capital Markets
2. Comovement
3. Volatility Spillover
4. Regime Switching Regression
5. Hidden Markov Model
Authors:
1 . Cecep Riswanda
Telkom University Bandung, Indonesia
2 . Brady Rikumahu
Telkom University Bandung, Indonesia
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How to Cite
Riswanda, C., & Rikumahu, B. (2024). Analysis of the effect of Regional Stock Market on the Jakarta Composite Index using Markov Regime Switching Regression. Journal of Multidisciplinary Academic Business Studies, 1(4), 863–874. https://doi.org/10.35912/jomabs.v1i4.2372
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Issue & Section
Vol. 1 No. 4 (2024): August
Articles
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References

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    Wegener, C., Kruse, R., & Basse, T. (2019). The walking debt crisis. Journal of Economic Behavior & Organization, 157, 382-402.

    Zhang, D., Hu, M., & Ji, Q. (2020). Financial markets under the global pandemic of COVID-19. Finance research letters, 36, 101528.

  1. Ahmad, W., & Sehgal, S. (2015). Regime shifts and volatility in BRIICKS stock markets: an asset allocation perspective. International Journal of Emerging Markets, 10(3), 383-408.
  2. Amin, M. Z., & Herawati, T. D. (2012). Pengaruh Tingkat Inflasi, Suku Bunga SBI, Nilai Kurs Dollar (USD/IDR), dan Indeks DowJones (DJIA) Terhadap Pergerakan Indeks Harga SahamGabungan Di Bursa Efek Indonesia (BEI)(Periode 2008-2011). Jurnal Skripsi, 13190276.
  3. Ang, A., & Bekaert, G. (1999). International asset allocation with time-varying correlations: National Bureau of Economic Research Cambridge, Mass., USA.
  4. Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2006). The cross?section of volatility and expected returns. The Journal of Finance, 61(1), 259-299.
  5. Baker, S. R., Bloom, N., Davis, S. J., & Terry, S. J. (2020). Covid-induced economic uncertainty. Retrieved from
  6. Bekaert, G., & Harvey, C. R. (2003). Market integration and contagion: National Bureau of Economic Research Cambridge, Mass., USA.
  7. Bekaert, G., Harvey, C. R., & Lundblad, C. (2011). Financial openness and productivity. World development, 39(1), 1-19.
  8. Campbell, J. Y., Lettau, M., Malkiel, B. G., & Xu, Y. (2001). Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk. The Journal of Finance, 56(1), 1-43.
  9. Chen, N.-F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. Journal of business, 383-403.
  10. Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158-171.
  11. Djirimu, M. A., & Tombolotutu, A. D. (2023). Dinamika Ekonomi Politik Internasional: Deepublish.
  12. Goutte, S. (2014). Conditional Markov regime switching model applied to economic modelling. Economic Modelling, 38, 258-269.
  13. Guidolin, M., & Timmermann, A. (2006). An econometric model of nonlinear dynamics in the joint distribution of stock and bond returns. Journal of applied econometrics, 21(1), 1-22.
  14. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica: journal of the Econometric Society, 357-384.
  15. Himawan, T. S., Indriyani, T., & Rahmawati, W. M. (2017). Implementasi Hidden Markov Model untuk Memprediksi Pergerakan Harga FOREX (Foreign Exchange).
  16. Krolzig, H.-M. (2013). Markov-switching vector autoregressions: Modelling, statistical inference, and application to business cycle analysis (Vol. 454): Springer Science & Business Media.
  17. Kusno, F. (2020). Krisis Politik Ekonomi Global Dampak Pandemi Covid-19. Anterior Jurnal, 19(2), 94-102.
  18. Larasati, D., Irwanto, A. K., & Permanasari, Y. (2013). Analisis strategi optimalisasi portofolio saham LQ 45 (pada Bursa Efek Indonesia Tahun 2009-2011). Jurnal manajemen dan organisasi, 4(2), 163-171.
  19. Leiwakabessy, A., Patty, M., & Baretha, M. T. (2021). Faktor Psikologi Investor Millenial dalam Pengambilan Keputusan Investasi Saham. Jurnal Akuntansi Dan Pajak, 22(02), 495.
  20. Lucey, B. M., & Voronkova, S. (2008). Russian equity market linkages before and after the 1998 crisis: Evidence from stochastic and regime-switching cointegration tests. Journal of International Money and Finance, 27(8), 1303-1324.
  21. Panopoulou, E., & Pantelidis, T. (2015). Regime-switching models for exchange rates. The European Journal of Finance, 21(12), 1023-1069.
  22. Phan, D. H. B., & Narayan, P. K. (2021). Country responses and the reaction of the stock market to COVID-19—A preliminary exposition Research on Pandemics (pp. 6-18): Routledge.
  23. Prasetyo, M. E. B. (2011). Teori Dasar Hidden Markov Model.
  24. Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257-286.
  25. Rigobon, R. (2019). Contagion, spillover, and interdependence. Economía, 19(2), 69-100.
  26. Rikumahu, B., & Anggraeni, A. (2021). Regime-switching regression for inferring the effect of the Hang Seng, S&P500, and SET indices on the Jakarta Composite Index. Paper presented at the The conference encourages submissions for paper presentations from academics and practitioners. In order to reach the goals of the sharing and exchange of experiences of both theoretical developments and applications, these presentations may have a focus on either research studies or case studies of best practices on related topics.
  27. Rizvi, S. A. R., Juhro, S. M., & Narayan, P. K. (2021). Understanding market reaction to COVID-19 monetary and fiscal stimulus in major ASEAN countries. Bulletin of Monetary Economics and Banking, 24(3), 313-334.
  28. Wegener, C., Kruse, R., & Basse, T. (2019). The walking debt crisis. Journal of Economic Behavior & Organization, 157, 382-402.
  29. Zhang, D., Hu, M., & Ji, Q. (2020). Financial markets under the global pandemic of COVID-19. Finance research letters, 36, 101528.