The Tripartite Spectrum of Risk Transmission in ASEAN Equity Markets: Monetary Anchors, Geopolitical Channels, and Commodity Frictions

Authors

  • ST. Dwiarso Utomo Universitas Dian Nuswantoro
  • Entot Suhartono Universitas Dian Nuswantoro
  • Ngurah Pandji M., A., D. Universitas Dian Nuswantoro
  • Bambang Minarso
  • Agung Prajanto
  • Shujahat Ali MUST Business School, Mirpur University of Science and Technology

DOI:

https://doi.org/10.56696/ijamer.v4i1.186

Keywords:

Market segmentation, Geopolitical risk transmission, ASEAN equity markets, Commodity-linked economies, Global financial cycle, Portfolio diversification, Shapley-value decomposition, Monetary policy frictions

Abstract

The prevailing assumption that ASEAN equity markets constitute a homogeneous asset class has profound consequences for regional portfolio construction, hedging strategies, and macroprudential coordination. This study challenges that assumption by identifying three structurally distinct risk-transmission regimes across the ASEAN-4 (Indonesia, Malaysia, Singapore, Thailand) over the 2016–2025 period. Employing a heterogeneous multi-task machine-learning framework augmented with Shapley-value decomposition on daily panel data encompassing global volatility (VIX), crude oil (Brent), crude palm oil (CPO), the US dollar index (DXY), US 10-year Treasury yields, local equity indices, bilateral exchange rates, and the BI-7DRR policy rate, we decompose each market's sensitivity profile into interpretable economic channels. Our analysis reveals a novel empirically grounded taxonomy—the Tripartite Spectrum of market integration: (i) Singapore operates as a Monetary-Driven Hub, with 64.0% of total Shapley attribution concentrated in the DXY and US 10-year yield, consistent with the global financial cycle hypothesis; (ii) Thailand functions as a Geopolitical Risk Transmission Channel, exhibiting disproportionate sensitivity to the VIX with a non-linear threshold effect that amplifies volatility by 340% when the VIX exceeds 35; and (iii) Indonesia and Malaysia constitute Commodity-Linked Segmented Markets, where CPO, Brent crude, and domestic monetary-policy frictions dominate price formation, with a superadditive CPO × BI-7DRR interaction effect (+280% amplification) unique to Indonesia. Kolmogorov-Smirnov tests decisively reject the homogeneous-region null (all cross-country p-values < 0.01). These findings carry direct implications for the limits of intra-ASEAN diversification, the design of regime-specific hedging strategies, and the calibration of commodity-price stress scenarios in macroprudential frameworks.

References

(BCBS), B. C. on B. S. (2023). BIS Quarterly Review (Issue March).

Aldasoro, I., Avdjiev, S., Borio, C., & Disyatat, P. (2023). Global and Domestic Financial Cycles : Variations on a Theme. International Journal of Central Banking, 19(5), 49–98. https://doi.org/10.1057/S41308-018-0072-6

Aryonindito, S., Salmana Dahyar, S., Gilang Cempaka, A., & Suryahadi Saputro, V. (2025). Market Reactions To Domestic And Global Policy Events: An Event Study Of The Indonesian Capital Market In Early 2025. International Journal of Management and Business Economics, 4(1), 1–5. https://doi.org/10.58540/ijmebe.v4i1.1011

Azimova, T. (2022). The Journal of Economic Asymmetries Modelling volatility transmission in regional Asian stock markets. The Journal of Economic Asymmetries, 26(March), e00274. https://doi.org/10.1016/j.jeca.2022.e00274

Balli, F., Ozer, H., Mudassar, B., & Allen, R. G. (2022). Geopolitical risk spillovers and its determinants. The Annals of Regional Science, 86(2), 463. https://doi.org/10.1007/s00168-021-01081-y

Basu, P., & Ray, P. (2022). China-plus-one : expanding global value chains. Journal of Business Strategy, 43(6), 350–356. https://doi.org/10.1108/JBS-04-2021-0066

Cakici, N., Fieberg, C., Metko, D., & Zaremba, A. (2023). Journal of Economic Dynamics and Control Machine learning goes global : Cross-sectional return predictability in international stock markets. Journal of Economic Dynamics and Control, 155(August), 104725. https://doi.org/10.1016/j.jedc.2023.104725

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

Cesa-bianchi, A., Martin, F. E., Thwaites, G., England, B., Street, T., & Ecr, L. (2019). Foreign booms , domestic busts : The global dimension of banking crises ☆. Journal of Financial Intermediation, 37(May 2017), 58–74. https://doi.org/10.1016/j.jfi.2018.07.001

Chancharat, S., & Chancharat, N. (2024). Asymmetric spillover and quantile linkage between the United States and ASEAN + 6 stock returns under uncertainty. Journal of Open Innovation: Technology, Market, and Complexity, 10(3), 100317. https://doi.org/10.1016/j.joitmc.2024.100317

Chege, C., Kithinji, M., & Gachoki, P. (2025). A Hybrid VAR-LSTM-GARCH Model for Multivariate Volatility Forecasting. International Journal of Data Science and Analysis, 11(4), 99–113. https://doi.org/10.11648/j.ijdsa.20251104.11

Chen, L., & Pelger, M. (2021). Deep Learning in Asset Pricing. Arxiv - Computers & Society.

Demirbaga, U. (2024). Empirical Asset Pricing Using Explainable Artificial Intelligence Empirical Asset Pricing Using Explainable Artificial Intelligence. University of Cambridg.

Errunza, V., & Losq, E. (1985). International Asset Pricing under Mild Segmentation: Theory and Test. The Journal of Finance, XL(1), 105–124. https://doi.org/https://doi.org/10.1111/j.1540-6261.1985.tb04939.x

Feng, L., Zheng, Y., Wang, X., Guo, C., & Xue, R. (2025). Global stock market forecasting : Insights from series and parallel combination of machine learning models. Pacific-Basin Finance Journal, 93(August), 102905. https://doi.org/10.1016/j.pacfin.2025.102905

Gerakoudi, K., Georgoulas, D., & Stavroulakis, P. J. (2025). A novel hybrid ARIMA-LSTM model for maritime shipping stock forecasting: comparative evidence against statistical and machine learning benchmarks. Maritime Business Review, 1–19. https://doi.org/10.1108/mabr-04-2024-0035

Goswami, G. G., Yahya, M., & Aftabi, M. (2025). Impact of financial and energy market uncertainties on ASEAN-5 markets. Eurasian Economic Review, 15(2025), 1261–1283. https://doi.org/10.1007/s40822-025-00327-w

Han, L., Ning, D., & Xin, S. (2026). Geopolitical risk and economic growth : Evidence from ASEAN countries. Finance Research Letters, 88(July 2025), 109102. https://doi.org/10.1016/j.frl.2025.109102

Han, S. (2025). Financial Time Series Forecasting : A Hybrid Approach Combining AR-GARCH and Machine Learning Models.

IMF, I. M. F. (2026). Commodity Special Feature : Market Developments and the Economics of Rare Earths The Economics of Rare Earths : Global Impact of Shortages and Industrial Policy (Issue October 2025).

Jinghua, Z., & Kogid, M. (2024). Stock Market Linkage between China and ASEAN-6 Countries : Integration Perspective. Jurnal Ekonomi Malaysia, 58(1), 1–10.

Kabir, R., Bhadra, D., & Ridoy, M. (2025). LSTM – Transformer-Based Robust Hybrid Deep Learning Model for Financial Time Series Forecasting.

Khraiche, M., Boudreau, J. W., & Chowdhury, S. R. (2023). Journal of International Financial Markets , Geopolitical risk and stock market development. Journal of International Financial Markets, Institutions & Money, 88(September), 101847. https://doi.org/10.1016/j.intfin.2023.101847

Lundberg, S. M., Erion, G., Chen, H., Degrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S. (2019). Explainable AI for Trees : From Local Explanations to Global Understanding. Arxiv - Computers & Society, 1–72.

Lundberg, S. M., & Lee, S. (2017). A unified approach to interpreting model predictions. Arxiv - Computers & Society, Section 3, 1–9.

Marisetty, N. (2025). Evaluating forecast accuracy for NSE Nifty : A cross- market study using OLS , HSC , GARCH , and VAR models. 7(2), 1181–1199.

Mariyani, D., Hariyanti, H., & Kalista, A. (2025). Shifting The Relationship Between Market Sentiment, Market Volume, Trading Volume and Volatility on The Indonesia Stock Exchange Approach Model Vector Autoregression. Eduvest - Journal of Universal Studies, 5(4), 3832–3842. https://doi.org/10.59188/eduvest.v5i4.34659

Mensi, W., Razzaq, A., Rababa, A., Vinh, X., & Hoon, S. (2021). Asymmetric spillover and network connectedness between crude oil , gold , and Chinese sector stock markets. Energy Economics, 98, 105262. https://doi.org/10.1016/j.eneco.2021.105262

Miranda-agrippino, S. (2020). U . S . Monetary Policy and the Global Financial Cycle. Review of Economics Studies, 87, 2754–2776. https://doi.org/10.1093/restud/rdaa019

Rahmawanto, S. B., Nugroho, D. B., & Trihandaru, S. (2025). Using an LSTM Neural Network to Improve Symmetric and Asymmetric GARCH Volatility Forecast. Zero : Jurnal Sains, Matematika, Dan Terapan, 9(1), 201–214. https://doi.org/10.30829/zero.v9i1.24614

Rohan, A., Hossen, D., & Pranto, N. (2026). Artificial intelligence in financial market prediction : advancements in machine learning for stock price forecasting. Frontiers in Artificial Intelligence, 8(January), 1–34. https://doi.org/10.3389/frai.2025.1696423

Sun, Z. (2025). A Comprehensive Review of Stock Index Prediction Methods: From Traditional Econometrics to Deep Learning with Attention Mechanisms. Transactions on Computer Science and Intelligent Systems Research, 9, 547–552. https://doi.org/10.62051/g34zas22

Ur Rehman, M., Vinh, X., Mciver, R., & Hoon, S. (2022). Sensitivity of US sectoral returns to energy commodities under different investment horizons and market conditions. Energy Economics, 108(February), 105878. https://doi.org/10.1016/j.eneco.2022.105878

Veronika, Y., & Efendi, B. (2025). The Effectiveness of Monetary , Macroprudential , and Microprudential Policies on Financial Stability in Indonesia During and Post-COVID-19. GOLDEN RATIO OF DATA IN SUMMARY, 5(4), 797–808. https://doi.org/https://doi.org/10.52970/grdis.v5i4.1633 ABSTRACT

Voora, V., Bermúdez, S., Farrell, J. J., Larrea, C., & Luna, E. (2023). Global Market Report: Palm oil prices and sustainability.

Wang, D., Li, P., & Huang, L. (2022). Time-frequency volatility spillovers between major international financial markets during the COVID-19 pandemic. Finance Research Letters, 46(PA), 102244. https://doi.org/10.1016/j.frl.2021.102244

Wang, K.-H., Wang, Z.-S., Liu, H.-W., & Li, X. (2023). Economic policy uncertainty and geopolitical risk: evidence from China and Southeast Asia. Asian-Pacific Economic Literature, 37(2). https://doi.org/https://doi.org/10.1111/apel.12388

Wang, Y., Chen, Z., & Ji, X. (2023). North American Journal of Economics and Finance Cross-market information transmission and stock market volatility prediction. North American Journal of Economics and Finance, 68(April), 101977. https://doi.org/10.1016/j.najef.2023.101977

Xiao, X., Hua, X., & Qin, K. (2024). A self-attention based cross-sectional return forecasting model with evidence from the Chinese market. Finance Research Letters, 62(PA), 105144. https://doi.org/10.1016/j.frl.2024.105144

Yeoh, K. Y., & Tham, K. (2025). Exploring the relationship between macroeconomic conditions and distressed real estate loans during the COVID-19 pandemic in Malaysia and Singapore. International Journal of Housing Markets and Analysis, 1–40. https://doi.org/10.1108/IJHMA-04-2025-0100

Yilmazkuday, H. (2024). European Journal of Political Economy. European Journal of Political Economy, 83(May), 102553. https://doi.org/10.1016/j.ejpoleco.2024.102553

Zaremba, A., Cakici, N., Demir, E., & Long, H. (2022). When bad news is good news : Geopolitical risk and the cross-section of emerging market stock returns. Journal of Financial Stability, 58(December 2021), 100964. https://doi.org/10.1016/j.jfs.2021.100964

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Published

2026-08-12

How to Cite

Utomo, S. D., Suhartono, E., Pandji M., A., D., N., Minarso, B., Prajanto, A., & Ali, S. (2026). The Tripartite Spectrum of Risk Transmission in ASEAN Equity Markets: Monetary Anchors, Geopolitical Channels, and Commodity Frictions. International Journal Of Accounting, Management, And Economics Research, 4(1). https://doi.org/10.56696/ijamer.v4i1.186