🤖 AI Summary
This study investigates the clustering of sovereign stress episodes—referred to as “avalanches”—in Latin American credit markets and their amplification through network dynamics. Leveraging EMBI spread data from eleven countries over 2007–2026, the authors develop an innovative threshold-based method to identify country-level stress events and employ heavy-tailed distribution tests, permutation controls, and dynamic network analysis to disentangle common-factor-driven co-movements from conditional dependence in regional contagion channels. Proposing a finite-size criticality framework to characterize emerging market fragility, the study finds that avalanche sizes follow a power-law distribution with exponent 1.77, and exhibit significantly higher synchronicity than random benchmarks (p<0.001). Large avalanches coincide with denser contemporaneous correlation networks, though partial correlation networks show no such pattern, suggesting that network metrics capture real-time stress conditions rather than serve as leading early-warning signals.
📝 Abstract
This paper studies sovereign stress avalanches and network amplification in Latin American credit markets using monthly J.P. Morgan EMBI Global Diversified spreads for eleven sovereigns over 2007-2026. Country stress events are defined as positive log-spread innovations exceeding country-specific volatility thresholds, and regional avalanches count the number of stressed countries in each month. The empirical design combines finite-sample power-law diagnostics, threshold robustness checks, a country-level reshuffling placebo, and rolling correlation, partial-correlation, and minimum-spanning-tree networks. Avalanche sizes are heavy-tailed, with an estimated exponent of 1.77, while spread changes and inter-event times lie in a heavy-tail boundary regime. The placebo shows synchronization far above independent stress timing, with p-values below 0.001. Large avalanches coincide with denser and more spectrally amplifying raw-correlation networks, but not after partial-correlation filtering, indicating common-factor co-movement rather than conditional regional propagation. Network metrics describe contemporaneous stress regimes rather than early-warning signals. The results provide a finite-size criticality framework for monitoring sovereign fragility in emerging markets.