Global Shock Transmission dan Sectoral Systemic Risk di Indonesia

Authors

DOI:

https://doi.org/10.26623/slsi.v24i3.14358

Abstract

Penelitian ini bertujuan untuk menganalisis dinamika keterhubungan antar sektor saham di Indonesia serta implikasinya terhadap risiko sistemik dan diversifikasi portofolio selama periode yang ditandai oleh berbagai guncangan global. Penelitian ini menggunakan data harian sebelas indeks sektoral yang diklasifikasikan dalam Indonesia Stock Exchange Industrial Classification (IDX–IC) selama periode Januari 2021 hingga Oktober 2025. Penelitian ini menerapkan pendekatan Time-Varying Parameter Vector Autoregression (TVP–VAR) yang dikombinasikan dengan generalized forecast error variance decomposition (GFEVD) untuk mengukur tingkat dan arah transmisi risiko antar sektor. Hasil penelitian menunjukkan bahwa tingkat keterhubungan antar sektor bersifat tinggi dan meningkat secara signifikan selama periode krisis, yang tercermin dari lonjakan Total Connectedness Index (TCI). Penelitian ini juga menemukan bahwa keterhubungan bersifat dinamis dan menunjukkan perubahan peran sektor sebagai transmiter dan penerima risiko dari waktu ke waktu. Sektor berbasis komoditas dan keuangan secara konsisten berperan sebagai sumber utama transmisi risiko, sedangkan sektor teknologi dan kesehatan cenderung berperan sebagai penerima risiko. Temuan ini mengindikasikan adanya asimetri dalam struktur keterhubungan sektoral serta penurunan efektivitas diversifikasi selama periode tekanan pasar. Selain itu, kendala institusional dalam pasar domestik berperan dalam memperkuat transmisi risiko antar sektor. Penelitian ini memberikan implikasi penting bagi investor dalam merancang strategi portofolio yang adaptif serta bagi regulator dalam memperkuat pengawasan risiko sistemik berbasis sectoral.

References

Adekoya, O. B., & Oliyide, J. A. (2021). How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques. Resources Policy, 70, 101898. https://doi.org/10.1016/j.resourpol.2020.101898

Adekoya, O. B., Oliyide, J. A., & Tiwari, A. K. (2021). Risk transmissions between sectoral Islamic and conventional stock markets during COVID-19 pandemic: What matters more between actual COVID-19 occurrence and speculative and sentiment factors? Borsa Istanbul Review, 22(2), 363–376. http://europepmc.org/abstract/PMC/PMC8947859

Ahmed, N., Areche, F., Sheikh, A., & Lahiani, A. (2022). Green Finance and Green Energy Nexus in ASEAN Countries: A Bootstrap Panel Causality Test. Energies, 15(14), 5068. https://doi.org/10.3390/en15145068

Allen, F., & Barbalau, A. (2024). Security design: A review. Journal of Financial Intermediation, 60, 101113. https://doi.org/10.1016/j.jfi.2024.101113

Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions. Journal of Risk and Financial Management, 13(4), 84. https://doi.org/10.3390/jrfm13040084

Antonakakis, N., Gabauer, D., & Gupta, R. (2019). International monetary policy spillovers: Evidence from a time-varying parameter vector autoregression. International Review of Financial Analysis, 65, 101382. https://doi.org/10.1016/j.irfa.2019.101382

Balcilar, M., Gabauer, D., & Umar, Z. (2021). Crude Oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach. Resources Policy, 73, 102219. https://doi.org/10.1016/j.resourpol.2021.102219

Baldwin, R., & Freeman, R. (2022). Risks and Global Supply Chains: What We Know and What We Need to Know. Annual Review of Economics, 14(1), 153–180. https://doi.org/10.1146/annurev-economics-051420-113737

Balli, F., Balli, H. O., Dang, T. H. N., & Gabauer, D. (2023). Contemporaneous and lagged R2 decomposed connectedness approach: New evidence from the energy futures market. Finance Research Letters, 57, 104168. https://doi.org/10.1016/j.frl.2023.104168

Baruník, J., & Křehlík, T. (2018). Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk*. Journal of Financial Econometrics, 16(2), 271–296. https://doi.org/10.1093/jjfinec/nby001

Bekaert, G., & Harvey, C. R. (1995). Time‐Varying World Market Integration. The Journal of Finance, 50(2), 403–444. https://doi.org/10.1111/j.1540-6261.1995.tb04790.x

Bhattacherjee, P., Mishra, S., & Kang, S. H. (2024). Extreme time-frequency connectedness across U.S. sector stock and commodity futures markets. International Review of Economics & Finance, 93, 1176–1197. https://doi.org/https://doi.org/10.1016/j.iref.2024.05.021

Biermann, M., & Leromain, E. (2025). The ripple effect: trade linkages and the stock market response to the Russia–Ukraine war. Review of World Economics, 161(4), 1637–1660. https://doi.org/10.1007/s10290-025-00594-4

Billah, M. (2025). Unraveling financial interconnectedness: A quantile VAR model analysis of AI-based assets, sukuk, and islamic equity indices. Research in International Business and Finance, 75, 102718. https://doi.org/10.1016/j.ribaf.2024.102718

Billio, M., Getmansky, M., Lo, A. W., & Pelizzon, L. (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics, 104(3), 535–559. https://doi.org/https://doi.org/10.1016/j.jfineco.2011.12.010

Bouri, E., Cepni, O., Gabauer, D., & Gupta, R. (2021). Return connectedness across asset classes around the COVID-19 outbreak. International Review of Financial Analysis, 73, 101646. https://doi.org/10.1016/j.irfa.2020.101646

Caldara, D., & Iacoviello, M. (2022). Measuring Geopolitical Risk. American Economic Review, 112(4), 1194–1225. https://doi.org/10.1257/aer.20191823

Chatziantoniou, I., Abakah, E. J. A., Gabauer, D., & Tiwari, A. K. (2022). Quantile time–frequency price connectedness between green bond, green equity, sustainable investments and clean energy markets. Journal of Cleaner Production, 361, 132088. https://doi.org/10.1016/j.jclepro.2022.132088

Chen, P., He, L., & Yang, X. (2021). On interdependence structure of China’s commodity market. Resources Policy, 74, 102256. https://doi.org/https://doi.org/10.1016/j.resourpol.2021.102256

Christoffersen, P. F. (2012). Elements of Financial Risk Management (Second Edi). Elsevier. https://doi.org/10.1016/C2009-0-22827-3

Chuliá, H., Muñoz-Mendoza, J. A., & Uribe, J. M. (2023). Energy firms in emerging markets: Systemic risk and diversification opportunities. Emerging Markets Review, 56, 101053. https://doi.org/10.1016/j.ememar.2023.101053

Cocca, T., Gabauer, D., & Pomberger, S. (2024). Clean energy market connectedness and investment strategies: New evidence from DCC-GARCH R2 decomposed connectedness measures. Energy Economics, 136, 107680. https://doi.org/10.1016/j.eneco.2024.107680

Cunado, J., Chatziantoniou, I., Gabauer, D., de Gracia, F. P., & Hardik, M. (2023). Dynamic spillovers across precious metals and oil realized volatilities: Evidence from quantile extended joint connectedness measures. Journal of Commodity Markets, 30, 100327. https://doi.org/10.1016/j.jcomm.2023.100327

Dang, T. H. N., Balli, F., Balli, H. O., Gabauer, D., & Nguyen, T. T. H. (2024). Sectoral uncertainty spillovers in emerging markets: A quantile time–frequency connectedness approach. International Review of Economics & Finance, 93, 121–139. https://doi.org/10.1016/j.iref.2024.04.017

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. https://doi.org/10.1111/j.1468-0297.2008.02208.x

Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006

Endri, E., Fauzi, F., & Effendi, M. S. (2024). Integration of the Indonesian Stock Market with Eight Major Trading Partners’ Stock Markets. Economies, 12(12), 350. https://doi.org/10.3390/economies12120350

Evrim Mandaci, P., Azimli, A., & Mandaci, N. (2023). The impact of geopolitical risks on connectedness among natural resource commodities: A quantile vector autoregressive approach. Resources Policy, 85, 103957. https://doi.org/10.1016/j.resourpol.2023.103957

Foglia, M., Maci, G., & Pacelli, V. (2024). FinTech and fan tokens: Understanding the risks spillover of digital asset investment. Research in International Business and Finance, 68, 102190. https://doi.org/10.1016/j.ribaf.2023.102190

Gabauer, D. (2021). Dynamic measures of asymmetric & pairwise connectedness within an optimal currency area: Evidence from the ERM I system. Journal of Multinational Financial Management, 60, 100680. https://doi.org/10.1016/j.mulfin.2021.100680

Ghaemi Asl, M., Adekoya, O. B., & Rashidi, M. M. (2023). Quantiles dependence and dynamic connectedness between distributed ledger technology and sectoral stocks: enhancing the supply chain and investment decisions with digital platforms. Annals of Operations Research, 327(1), 435–464. https://doi.org/10.1007/s10479-022-04882-2

Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357–384. https://doi.org/10.2307/1912559

Helmi, M. H., Cui, J., Elsayed, A. H., & Hoque, M. E. (2025). Higher-order moment and cross-moment spillovers among MENA stock markets: Insights from geopolitical risks and global fear. Research in International Business and Finance, 77, 102885. https://doi.org/10.1016/j.ribaf.2025.102885

Kang, S. H., Uddin, G. S., Troster, V., & Yoon, S.-M. (2019). Directional spillover effects between ASEAN and world stock markets. Journal of Multinational Financial Management, 52–53, 100592. https://doi.org/10.1016/j.mulfin.2019.100592

Kayani, U., Iqbal, U., Aysan, A. F., Fianto, B. A., Rabbani, M. R., & Hasan, F. (2025). Revealing the secrets of working capital: A comparison between sharia-compliant and conventional firms. Economic Systems, 49(2), 101278. https://doi.org/10.1016/j.ecosys.2024.101278

Kayani, U. N., Aysan, A. F., Khan, M., Khan, M., Mumtaz, R., & Irfan, M. (2024). Unleashing the pandemic volatility: A glimpse into the stock market performance of developed economies during COVID-19. Heliyon, 10(4), e25202. https://doi.org/10.1016/j.heliyon.2024.e25202

Koop, G., & Korobilis, D. (2013). Large time-varying parameter VARs. Journal of Econometrics, 177(2), 185–198. https://doi.org/10.1016/j.jeconom.2013.04.007

Li, Z., Pei, S., Li, T., & Wang, Y. (2023). Risk spillover network in the supply chain system during the COVID-19 crisis: Evidence from China. Economic Modelling, 126, 106403. https://doi.org/10.1016/j.econmod.2023.106403

Malmstrom Rognes, A., & Larsson, M. (2023). Can regulations prevent financial crises? Uses of the past in the evolution of regulatory reforms in Sweden. Journal of Financial Regulation and Compliance, 31(4), 469–482. https://doi.org/10.1108/JFRC-06-2022-0078

Mateus, C., Bagirov, M., & Mateus, I. (2024). Return and volatility connectedness and net directional patterns in spillover transmissions: East and Southeast Asian equity markets. International Review of Finance, 24(1), 83–103. https://doi.org/10.1111/irfi.12435

Mishra, A. K., Anand K, K., & Venkatasai Kappagantula, A. (2025). Unveiling asymmetric return spillovers with portfolio implications among Indian stock sectors during Covid-19 pandemic. The North American Journal of Economics and Finance, 75, 102297. https://doi.org/https://doi.org/10.1016/j.najef.2024.102297

Murè, P., Paccione, C., Marzioni, S., & Giorgio, S. (2024). How electricity and natural gas prices affect banking systemic risk. Research in International Business and Finance, 72, 102510. https://doi.org/10.1016/j.ribaf.2024.102510

Naeem, M. A., Chatziantoniou, I., Gabauer, D., & Karim, S. (2024). Measuring the G20 stock market return transmission mechanism: Evidence from the R2 connectedness approach. International Review of Financial Analysis, 91, 102986. https://doi.org/10.1016/j.irfa.2023.102986

Naeem, M. A., Gul, R., Arfaoui, N., Bakry, W., & Bhatti, M. I. (2025). Riding the storm: AI-driven spillover effects across technology, commodities, and conventional markets. Pacific-Basin Finance Journal, 93, 102845. https://doi.org/10.1016/j.pacfin.2025.102845

Naeem, M. A., Yousaf, I., Karim, S., Yarovaya, L., & Ali, S. (2023). Tail-event driven NETwork dependence in emerging markets. Emerging Markets Review, 55, 100971. https://doi.org/10.1016/j.ememar.2022.100971

Naveed, M., Ali, S., Gubareva, M., & Omri, A. (2024). When giants fall: Tracing the ripple effects of Silicon Valley Bank (SVB) collapse on global financial markets. Research in International Business and Finance, 67, 102160. https://doi.org/10.1016/j.ribaf.2023.102160

Nguyen, A. T. H., & Le, T. T. (2025). In bank runs and market stress, it matters how networks impact: Exploring the financial connectedness in Vietnam. Finance Research Letters, 72, 106489. https://doi.org/https://doi.org/10.1016/j.frl.2024.106489

O’Hara, M. (2015). High-frequency market microstructure. Journal of Financial Economics, 116(2), 257–270. https://doi.org/10.1016/j.jfineco.2015.01.003

Papathanasiou, S., Syriopoulos, T., Kenourgios, D., & Koutsokostas, D. (2025). Sailing through uncertainty: Shipping’s role in financial shock transmission and hedging strategies. Global Finance Journal, 67, 101159. https://doi.org/10.1016/j.gfj.2025.101159

Schweizer, D., Wang, X., Wu, G., & Zhang, A. (2025). Political connections and media bias: Evidence from China. Journal of Corporate Finance, 94, 102835. https://doi.org/10.1016/j.jcorpfin.2025.102835

Scott, W. R. (2008). Institutions and organizations: Ideas and interests. In Institutions and Organizations: Ideas and Interests.

Shiller, R. J. (1981). Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends? The American Economic Review, 71(3), 421–436. http://www.jstor.org/stable/1802789

Singh, V. K., Kumar, P., & Nishant, S. (2019). Global connectedness of MSCI energy equity indices: A system-wide network approach. Energy Economics, 84, 104477. https://doi.org/https://doi.org/10.1016/j.eneco.2019.104477

Tabash, M. I., Sheikh, U. A., Shawkat, H., & Sang Hoon, K. (2026). From collapse to contagion: The Silicon Valley Bank (SVB) default and its ripple effects across global islamic and conventional financial sectors. Research in International Business and Finance, 81, 103142. https://doi.org/https://doi.org/10.1016/j.ribaf.2025.103142

Umar, Z., Mokni, K., & Escribano, A. (2022). Connectedness between the COVID-19 related media coverage and Islamic equities: The role of economic policy uncertainty. Pacific-Basin Finance Journal, 75, 101851. https://doi.org/https://doi.org/10.1016/j.pacfin.2022.101851

Umar, Z., Polat, O., Choi, S.-Y., & Teplova, T. (2022). The impact of the Russia-Ukraine conflict on the connectedness of financial markets. Finance Research Letters, 48, 102976. https://doi.org/10.1016/j.frl.2022.102976

Xu, D., Hu, Y., Corbet, S., & Lang, C. (2024). Return connectedness of green bonds and financial investment channels in China: Implications for hedging and regulation. Research in International Business and Finance, 70, 102329. https://doi.org/10.1016/j.ribaf.2024.102329

Younis, I., Gupta, H., Du, A. M., Shah, W. U., & Hanif, W. (2024). Spillover dynamics in DeFi, G7 banks, and equity markets during global crises: A TVP-VAR analysis. Research in International Business and Finance, 70, 102405. https://doi.org/10.1016/j.ribaf.2024.102405

Zahoor, N., Wu, J., Khan, H., & Khan, Z. (2023). De-globalization, International Trade Protectionism, and the Reconfigurations of Global Value Chains. Management International Review, 63(5), 823–859. https://doi.org/10.1007/s11575-023-00522-4

Downloads

Published

2026-07-16

Issue

Section

Articles

How to Cite

Purnomo, D. T., & Lestari, R. I. . (2026). Global Shock Transmission dan Sectoral Systemic Risk di Indonesia. Solusi, 24(3), 354-369. https://doi.org/10.26623/slsi.v24i3.14358