When policy uncertainty hits iron ore: dynamic spillovers to steel-related stock markets

Using data from September 2011 to November 2023, this paper examines how shocks to economic policy uncertainty (EPU) and the China Iron Ore Price Index (CIOPI) propagate along China’s iron ore–steel chain. TVP-VAR estimates show the clearest adverse short-run EPU response in the steel-sector index; the direct CIOPI response is not distinguishable from zero, while downstream responses are heterogeneous. An unexpected CIOPI increase raises steel and selected downstream stock indices only briefly, and forward–reverse asymmetry is not established. Local projections show that higher EPU is followed by declines in steel-industry new orders, greater order-adjustment intensity, and short-run changes in steel and port inventories, consistent with an order–inventory adjustment channel. The spillover index is 68.77%, with machinery as the principal net transmitter. Removing the CSI 300 component reduces connectedness to 21.83%, but machinery retains this role. The findings are robust to BVAR estimation, alternative lag orders, and variance-decomposition horizons.

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Publication Details

Journal
Journal of Applied Economics
Published
2026-11-06
DOI
https://doi.org/10.1080/15140326.2026.2751135
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
0.00

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article

When policy uncertainty hits iron ore: dynamic spillovers to steel-related stock markets

Hui Li, Sensheng Li
Journal of Applied Economics
Market Dynamics and Volatility
article

When policy uncertainty hits iron ore: dynamic spillovers to steel-related stock markets

Hui Li, Sensheng Li
article en

Abstract

Using data from September 2011 to November 2023, this paper examines how shocks to economic policy uncertainty (EPU) and the China Iron Ore Price Index (CIOPI) propagate along China’s iron ore–steel chain. TVP-VAR estimates show the clearest adverse short-run EPU response in the steel-sector index; the direct CIOPI response is not distinguishable from zero, while downstream responses are heterogeneous. An unexpected CIOPI increase raises steel and selected downstream stock indices only briefly, and forward–reverse asymmetry is not established. Local projections show that higher EPU is followed by declines in steel-industry new orders, greater order-adjustment intensity, and short-run changes in steel and port inventories, consistent with an order–inventory adjustment channel. The spillover index is 68.77%, with machinery as the principal net transmitter. Removing the CSI 300 component reduces connectedness to 21.83%, but machinery retains this role. The findings are robust to BVAR estimation, alternative lag orders, and variance-decomposition horizons.

Journal of Applied EconomicsVol. 30(1)
Sichuan Agricultural University (CN), PetroChina Southwest Oil and Gas Field Company (China) (CN)
PetroChina Company Limited, Natural Science Foundation of Sichuan Province
Openalex Percentile: Top 12%
Market Dynamics and Volatility
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