Dynamic Risk in the U.S. Banking System: An Analysis of Sentiment, Policy Shocks, and Spillover Effects

ArXiv ID: 2601.01783 “View on arXiv”

Authors: Haibo Wang, Jun Huang, Lutfu S Sua, Jaime Ortiz, Jinshyang Roan, Bahram Alidaee

Abstract

The 2023 U.S. banking crisis propagated not through direct financial linkages but through a high-frequency, information-based contagion channel. This paper moves beyond exploration analysis to test the “too-similar-to-fail” hypothesis, arguing that risk spillovers were driven by perceived similarities in bank business models under acute interest rate pressure. Employing a Time-Varying Parameter Vector Autoregression (TVP-VAR) model with 30-day rolling windows, a method uniquely suited for capturing the rapid network shifts inherent in a panic, we analyze daily stock returns for the four failed institutions and a systematically selected peer group of surviving banks vulnerable to the same risks from March 18, 2022, to March 15, 2023. Our results provide strong evidence for this contagion channel: total system connectedness surged dramatically during the crisis peak, and we identify SIVB, FRC, and WAL as primary net transmitters of risk while their perceived peers became significant net receivers, a key dynamic indicator of systemic vulnerability that cannot be captured by asset-by-asset analysis. We further demonstrate that these spillovers were significantly amplified by market sentiment (as measured by the VIX) and economic policy uncertainty (EPU). By providing a clear conceptual framework and robust empirical validation, our findings confirm the persistence of systemic risks within the banking network and highlight the importance of real-time monitoring in strengthening financial stability.

Keywords: TVP-VAR, network contagion, systemic risk, rolling window analysis, risk spillovers, Banking/Financials

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper employs a complex econometric model (TVP-VAR) with a 3C framework, requiring significant mathematical understanding, while simultaneously using high-frequency daily data, a specific methodology (rolling windows), and clear empirical validation of spillover dynamics.

  flowchart TD
    A["Research Goal: Identify and quantify risk transmission in the 2023 US banking crisis"] --> B["Data Selection: 30-day rolling window of daily stock returns<br/>from March 2022 to March 2023"]
    B --> C["Methodology: Time-Varying Parameter Vector Autoregression (TVP-VAR) model"]
    C --> D["Computational Process: Analyze dynamic network connectedness<br/>and volatility spillovers"]
    D --> E["Key Findings: Risk propagates via information contagion<br/>and 'too-similar-to-fail' dynamics"]
    E --> F["Outcome: Identified primary transmitters (SIVB, FRC, WAL)<br/>and amplification by VIX/EPU sentiment"]