General Equilibrium Amplification and Crisis Vulnerability: Cross-Crisis Evidence from Global Banks
一般均衡放大与危机脆弱性：全球银行的跨危机证据AI 解读 ↓
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一般均衡放大与危机脆弱性：全球银行的跨危机证据
- 研究问题
- 如何同时刻画地理邻近和银行间网络的压力传播，并衡量局部冲击经过整个金融系统放大后对银行危机脆弱性的影响？
- 方法与思路
- 作者构建连续空间—网络传播主方程，借 Feynman–Kac 表示推导总效应与直接效应之比，并分解地理、网络及交互渠道。
- 材料陈述的贡献
- 摘要称放大因子可分解系统重要性，并将离散级联模型纳入极限情形；38 家全球银行的跨危机分析显示其与危机表现相关。
- 阅读关注点
- 应核对传播方程与放大因子的识别及校准、危机外预测划分，以及相关检验的不确定性和异常收益稳健性分析。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要报告的部分相关检验 p 值为 0.080，相关证据不足以验证结构因果机制。
原文摘要
This paper develops a continuous framework for analyzing financial contagion that incorporates both geographic proximity and interbank network linkages. The framework characterizes stress propagation through a master equation whose solution admits a Feynman-Kac representation as expected cumulative stress along stochastic paths through spatial-network space. From this representation, I derive the General Equilibrium Amplification Factor -- a structural measure of systemic importance that captures the ratio of total system-wide effects to direct effects following a localized shock. The amplification factor decomposes naturally into spatial, network, and interaction components, revealing which transmission channels contribute most to each institution's systemic importance. The framework nests discrete cascade models as a limiting case when jump intensity becomes infinite above default thresholds, clarifying that continuous and discrete approaches describe different regimes of the same phenomenon. Empirical validation using 38 global banks across the 2008 financial crisis and COVID-19 pandemic demonstrates that the amplification factor correctly identifies systemically important institutions (Pearson correlation $ρ= -0.450$, $p = 0.080$ between amplification factor and crisis drawdowns) and predicts crisis outcomes out-of-sample ($ρ= -0.352$ for COVID-19). Robustness analysis using cumulative abnormal returns -- a measure more directly connected to the Feynman-Kac integral -- strengthens these findings ($ρ= -0.512$, $p = 0.042$). Time-series analysis confirms that average pairwise bank correlations track macroeconomic stress indicators ($ρ= 0.265$ with VIX, $p < 0.001$). Comparing the two crises reveals that COVID-19 produced a sharper correlation spike (+93%) despite smaller equity losses, reflecting different contagion dynamics for exogenous versus endogenous shocks.