Market Risk Connectedness and Tail Risk in the Global High-Bandwidth Memory Oligopoly

Authors

  • Chuantong Yang The Information School, University of Washington, Seattle, WA, United States

Keywords:

Artificial intelligence (AI) semiconductors, High-Bandwidth Memory (HBM), Micron, Samsung Electronics, SK Hynix

Abstract

High-Bandwidth Memory (HBM) is a critical AI-semiconductor bottleneck concentrated among Micron, Samsung Electronics, and SK Hynix. Using 1,895 aligned daily USD log returns (2018–2025), this study combines asymmetric GARCH, rolling Diebold-Yilmaz connectedness, and 99% Value-at-Risk (VaR) and Expected Shortfall (ES). The five-variable network has a total connectedness index (TCI) of 38.1%, versus 20.0% for the memory-only triad. Contrary to the initial hypothesis, post-AI connectedness falls from about 43% to 39%. HBM suppliers are net receivers—especially SK Hynix—while SOX and NVIDIA transmit risk. The equal-weighted HBM portfolio's tail risk rises in the AI regime but remains below SOX. HBM firms are therefore best interpreted as downstream risk-absorbing nodes with a supplier-specific connectedness component.

References

Micron Technology. “Micron commences volume production of industry-leading HBM3E,” 2024. investors.micron.com.

Counterpoint Research. Global DRAM and HBM market share, 2025. counterpointresearch.com.

Yole Group. Memory market surges beyond expectations, driven by HBM and AI, 2025. yolegroup.com.

TrendForce. Tight DRAM supply gives suppliers greater HBM pricing power, 2026. trendforce.com.

Engle, R.F. Autoregressive conditional heteroscedasticity. Econometrica 50(4), 1982, 987–1007. DOI: 10.2307/1912773.

Bollerslev, T. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31(3), 1986, 307–327. DOI: 10.1016/0304-4076(86)90063-1.

Nelson, D.B. Conditional heteroskedasticity in asset returns. Econometrica 59(2), 1991, 347–370. DOI: 10.2307/2938260.

Glosten, L.R., Jagannathan, R., & Runkle, D.E. Expected returns and volatility. Journal of Finance 48(5), 1993, 1779–1801. DOI: 10.1111/j.1540-6261.1993.tb05128.x.

Diebold, F.X., & Yilmaz, K. Predictive directional measurement of volatility spillovers. International Journal of Forecasting 28(1), 2012, 57–66. DOI: 10.1016/j.ijforecast.2011.02.006.

Diebold, F.X., & Yilmaz, K. Network topology of variance decompositions. Journal of Econometrics 182(1), 2014, 119–134. DOI: 10.1016/j.jeconom.2014.04.012.

Artzner, P., et al. Coherent measures of risk. Mathematical Finance 9(3), 1999, 203–228. DOI: 10.1111/1467-9965.00068.

Acerbi, C., & Tasche, D. Coherence of expected shortfall. Journal of Banking & Finance 26(7), 2002, 1487–1503. DOI: 10.1016/S0378-4266(02)00283-2.

Kupiec, P.H. Verifying risk-measurement models. Journal of Derivatives 3(2), 1995, 73–84. DOI: 10.3905/jod.1995.407942.

Christoffersen, P.F. Evaluating interval forecasts. International Economic Review 39(4), 1998, 841–862. DOI: 10.2307/2527341.

Caldara, D., & Iacoviello, M. Measuring geopolitical risk. American Economic Review 112(4), 2022, 1194–1225. DOI: 10.1257/aer.20191823.

Tan, X., et al. COVID-19 shock and time-varying volatility spillovers. Frontiers in Public Health 10, 2022, 906969. DOI: 10.3389/fpubh.2022.906969.

Snarska, M., et al. Semiconductor geopolitical and equity-market uncertainty transmission. International Review of Financial Analysis 106, 2025, 104457. DOI: 10.1016/j.irfa.2025.104457.

Zhou, X., & Liu, H. U.S.–China technology-equity risk spillovers and tail dependence. International Journal of Financial Studies 13(4), 2025, 242. DOI: 10.3390/ijfs13040242.

Ali, S., et al. Dynamic tail-risk connectedness between AI and fintech stocks. Annals of Operations Research 357(1), 2026, 373–407. DOI: 10.1007/s10479-024-06349-y.

JEDEC. JESD270-4 HBM4 DRAM standard, 2025. jedec.org.

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Published

2026-10-01

How to Cite

(1)
Yang, C. Market Risk Connectedness and Tail Risk in the Global High-Bandwidth Memory Oligopoly. FE 2026, 4, 11072.