Systemic-risk monitoring frameworks are largely built on absolute Pearson correlation networks: Assets are linked if their returns co-move on average, and "systemic hubs" are defined by high degree. Such approaches implicitly assume (ⅰ) a single contagion timescale and (ⅱ) stability of dependence, even though crises typically unfold in layers: A fast equity/volatility unwind, followed by slower stress in funding, FX, rates, and commodities. We proposed a stress-aware, multiscale alternative, and constructed the multiscale coherence–entropy centrality (MCEC) network in which (a) an edge between two assets exists only if their wavelet coherence is statistically significant and persistent across adjacent frequency bands, and (b) node importance is an entropy-weighted multi-horizon strength that is high only if an asset is strongly connected and active across time scales. We then generated a synthetic stressed panel by shocking all assets with a common heavy-tailed t-copula draw scaled by GARCH(1,1) volatilities, and compared MCEC to a traditional absolute-correlation backbone using 2021–2024 data. We reported three findings that are directly relevant for macroprudential supervision. First, under stress, the MCEC network reallocated centrality toward canonical stress transmitters (U.S. equity benchmarks, implied volatility (VIX), dollar/FX, long-term yields, crude oil, and gold), while ordinary correlation networks continued to present a single equity-dominated block. Second, MCEC delivered higher ex-ante classification performance (AUC) in identifying those transmitters even before the stressed regime was applied, indicating early-warning value. Third, MCEC made the stress-driven rewiring of cross-market spillover channels explicit across horizons rather than treating dependence as static.
Citation: Çağlar Sözen. Stress-aware multiscale spillover networks: Cross-market transmission via coherence–entropy centrality[J]. AIMS Mathematics, 2025, 10(11): 26313-26333. doi: 10.3934/math.20251157
Systemic-risk monitoring frameworks are largely built on absolute Pearson correlation networks: Assets are linked if their returns co-move on average, and "systemic hubs" are defined by high degree. Such approaches implicitly assume (ⅰ) a single contagion timescale and (ⅱ) stability of dependence, even though crises typically unfold in layers: A fast equity/volatility unwind, followed by slower stress in funding, FX, rates, and commodities. We proposed a stress-aware, multiscale alternative, and constructed the multiscale coherence–entropy centrality (MCEC) network in which (a) an edge between two assets exists only if their wavelet coherence is statistically significant and persistent across adjacent frequency bands, and (b) node importance is an entropy-weighted multi-horizon strength that is high only if an asset is strongly connected and active across time scales. We then generated a synthetic stressed panel by shocking all assets with a common heavy-tailed t-copula draw scaled by GARCH(1,1) volatilities, and compared MCEC to a traditional absolute-correlation backbone using 2021–2024 data. We reported three findings that are directly relevant for macroprudential supervision. First, under stress, the MCEC network reallocated centrality toward canonical stress transmitters (U.S. equity benchmarks, implied volatility (VIX), dollar/FX, long-term yields, crude oil, and gold), while ordinary correlation networks continued to present a single equity-dominated block. Second, MCEC delivered higher ex-ante classification performance (AUC) in identifying those transmitters even before the stressed regime was applied, indicating early-warning value. Third, MCEC made the stress-driven rewiring of cross-market spillover channels explicit across horizons rather than treating dependence as static.
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Çağlar Sözen. Stress-aware multiscale spillover networks: Cross-market transmission via coherence–entropy centrality[J]. AIMS Mathematics, 2025, 10(11): 26313-26333. doi: 10.3934/math.20251157
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