A Dynamic Information-Theoretic Network Model for Systemic Risk Assessment with an Application to China’s Maritime Sector

Lin Xiao, Arash Sioofy Khoojine (), Hao Chen and Congyin Wang
Additional contact information
Lin Xiao: Faculty of Economics and Business Administration, Yibin University, Yibin 644000, China
Arash Sioofy Khoojine: Faculty of Economics and Business Administration, Yibin University, Yibin 644000, China
Hao Chen: Faculty of Economics and Business Administration, Yibin University, Yibin 644000, China
Congyin Wang: Faculty of Economics and Business Administration, Yibin University, Yibin 644000, China

Mathematics, 2025, vol. 13, issue 18, 1-25

Abstract: This paper develops a dynamic information-theoretic network framework to quantify systemic risk in China’s maritime–commodity nexus with a focus on the Yangtze River Basin using eight monthly indicators, CCFI, CBCFI, BDI, YRCFI, GAUP, MPCT, CPUS, and ASMC. We resample, impute, standardize, and difference series to achieve stationary time series. Nonlinear interdependencies are estimated via KSG mutual information (MI) within sliding windows; networks are filtered using the Planar Maximally Filtered Graph (PMFG) with bootstrap edge validation (95th percentile) and benchmarked against the MST. Average MI indicates moderate yet heterogeneous dependence (about 0.13–0.17), revealing a container/port core (CCFI–YRCFI–MPCT), a bulk/energy spine (BDI–CPUS), and commodity bridges via GAUP. Dynamic PMFG metrics show a generally resilient but episodically vulnerable structure: density and compactness decline in turbulence. Stress tests demonstrate high redundancy to diffuse link failures (connectivity largely intact until ∼70–80% edge removal) but pronounced sensitivity of diffusion capacity to targeted multi-node outages. Early-warning indicators based on entropy rate and percolation threshold Z-scores flag recurring windows of elevated fragility; change point detection evaluation of both metrics isolates clustered regime shifts (2015–2016, 2018–2019, 2021–2022, and late 2023–2024). A Systemic Importance Index (SII) combining average centrality and removal impact ranks MPCT and CCFI as most critical, followed by BDI, with GAUP/CPUS mid-peripheral and ASMC peripheral. The findings imply that safeguarding port throughput and stabilizing container freight conditions deliver the greatest resilience gains, while monitoring bulk/energy linkages is essential when macro shocks synchronize across markets.

Keywords: systemic risk; information theory; complex networks; resilience; early warning; China maritime sector (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/18/2959/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/18/2959/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:18:p:2959-:d:1748365

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().