---
title: State-dependent global banking systemic risk: An integrated framework of network connectedness, tail risk, and global financial conditions
url: https://www.ml-quant.com/papers/ssrn/7493706/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 7493706
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7493706
featured: 2026-09-25
citations: unknown
topic: Risk, Credit & Banking
---


# State-dependent global banking systemic risk: An integrated framework of network connectedness, tail risk, and global financial conditions

Combining quantile-connectedness, tail-risk measures, and network analysis, the research shows tail connectedness exceeds median levels and lower-tail effects persist longer, with the VIX alone reliably predicting next-week systemic risk.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7493706
- Identifier: SSRN 7493706
- Released: 2026-09-20
- First featured: Quant Letter No. 132 (2026-09-25): https://www.ml-quant.com/issues/2026-09-25/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Risk, Credit & Banking
- Authors: Oguzhan Ozcelebi, Rim Khoury, Elhoussin Ouassou, Zhuhua Jiang, Mikhail Stolbov, Seong-Min Yoon

## Related

- [On joint marginal expected shortfall and associated contribution risk measures](https://www.ml-quant.com/papers/arxiv/2405.07549/): The paper introduces a new systemic risk measure, the joint marginal expected shortfall (JMES), to assess the impact of one entity's risk on another or overall risk, and compares its effectiveness with other popular measures.
- [Navigating Market Turbulence: Insights from Causal Network Contagion Value at Risk](https://www.ml-quant.com/papers/arxiv/2402.06032/): The paper presents the Causal-NECOVaR, a new method for financial risk analysis that provides reliable risk predictions regardless of market shocks and systemic changes.
- [Unveiling Early Warning Signals of Systemic Risks in Banks: A Recurrence Network-Based Approach](https://www.ml-quant.com/papers/arxiv/2310.10283/): The research introduces a method using high-frequency data to detect early signs of potential bank crises, proving that certain indicators can predict periods of high volatility in banking.
- [Contagion Effects of the Silicon Valley Bank Run](https://www.ml-quant.com/papers/arxiv/2308.06642/): The study analyzes the impact of Silicon Valley Bank's failure on other banks, highlighting the role of uninsured deposits and bank size, with mid-sized banks being most affected.
- [Large Banks and Systemic Risk: Insights from a Mean-Field Game Model](https://www.ml-quant.com/papers/arxiv/2305.17830/): Insights from a Game Model: Study investigates impact of large banks on financial system stability.
- [Systemic Risk Measures from 1927-2023](https://www.ml-quant.com/papers/ssrn/5030262/): Measures of systemic risk based on the comovements of US financial firms' stock returns under stress can predict market outcomes, bank failures, and balance-sheet results from 1927 to 2023.
