This study examines risk connectedness among sustainable and digital assets from 24 September 2019 to 26 May 2025. The sample contains three green finance indices, five literature-based green cryptocurrencies, and five literature-based non-green/conventional cryptocurrencies. We estimate conditional volatility, skewness, and kurtosis using the GARCHSK model and then apply time-domain, frequency-domain, and TVP-VAR connectedness frameworks to identify how shocks are shared across assets, moments, and investment horizons. The results show that the sustainable–digital finance system is strongly connected but unevenly organized. In the baseline static estimates, total connectedness is 75.87 for volatility, 65.11 for skewness, and 77.94 for kurtosis. Volatility and kurtosis connectedness are mainly long-run phenomena, whereas skewness connectedness is split almost evenly between short- and long-run components. Green finance assets mainly occupy net receiver positions in the estimated connectedness network, while several cryptocurrencies, including some classified as green in the literature, appear as net transmitters of volatility or tail-risk connectedness. Additional robustness checks using a monthly frequency cutoff and a pre-/post-Ethereum Merge split support the main horizon-level interpretation while also showing that the strength of connectedness changes across regimes. The findings provide connectedness-based diagnostics for portfolio design, system-level risk monitoring, and governance of sustainable–digital finance. They should be interpreted as evidence on forecast-error variance connectedness and relative transmitter/receiver roles rather than as structural causal estimates or direct measures of systemic losses.

Supply chain disruptions have become a major source of macro-financial stress, yet their implications for green finance remain underexplored. This paper investigates the state-dependent connectedness between supply-side bottlenecks and the green finance market, represented by clean energy equities, green bonds, and carbon prices. Using daily data on regional Supply Bottleneck Indices (SBIs) for China, the United States, and the euro area, we first construct a global Supply Bottleneck Index (GSBI) by principal component analysis and then estimate pairwise quantile-on-quantile connectedness (QQC) between supply bottleneck indicators and each green finance submarket. The results show that connectedness is strongly nonlinear, asymmetric, and time-varying. For the global indicator, connectedness intensifies at both joint and cross-tail quantile combinations, while mid-quantile states exhibit weak coupling. Regional results reveal clear heterogeneity: China and the United States display the strongest connectedness with clean energy equities in extreme upper-tail states, whereas the euro-area indicator is most tightly linked with the carbon market. Across many extreme states, supply bottleneck indicators show positive net connectedness with green finance markets, but green finance markets, especially carbon prices, can dominate the bilateral connectedness relation under calmer or intermediate regimes. Robustness checks based on average and quantile-rank GSBI constructions, a post-2023 subsample, and alternative QQC tuning choices support the tail-dominance pattern. These findings suggest that supply bottlenecks are not uniformly related to all green assets; rather, they are associated with state-dependent changes in the internal connectedness architecture of the green finance system. The paper contributes to the literature on financial connectedness and sustainable finance by showing how a real-economy disturbance is associated with changes in the connectedness and resilience of green financial markets.

This paper examines return connectedness among four channels for obtaining U.S.-dollar exposure in Argentina: the official USD/ARS rate, the informal Blue Dollar rate, and USDT/ARS prices on Binance and Bitso. We use 669 matched-date daily returns and a time-varying parameter VAR connectedness model with Barunik–Krehlik frequency decomposition. The analysis is descriptive: generalized forecast-error variance shares measure directional dependence within the estimated system but do not identify structural causality, price discovery, or systemic risk. Baseline estimates indicate that total connectedness rises from 43.57% under exchange controls and 45.26% during the crawling-peg period to 64.53% after liberalization. The two stablecoin venues have positive net-connectedness positions in the baseline estimates, while the official rate becomes a net receiver in Periods 2 and 3. Connectedness is concentrated within five- and ten-day horizons, although the share attributed to a strict three-day band is materially lower. Period 3 transmitter signs remain stable in contiguous subsamples, single-venue and aggregate-stablecoin specifications, and a six-variable system augmented with Bitcoin and a broad U.S.-dollar factor. By contrast, Period 2 directional rankings are more sensitive to alternative period boundaries and stablecoin representations and should therefore be interpreted cautiously. Rankings are also less stable in Period 1 and under deliberate one-day shifts in stablecoin closing dates, highlighting the importance of outliers and market synchronization. The results therefore provide conditional evidence on changing return interactions across segmented dollar-access venues rather than causal evidence that one venue drives another.

Weighted directed networks can exhibit stronger aggregate interactions without becoming more integrated or topologically richer. Using daily prices for 32 stock indices over 2006–2026, we construct effective transfer entropy (ETE) networks from range-based variance states and recompute GLMY path homology over a directed edge-density filtration. In 88 overlapping 120-common-date windows, total ETE is associated with a higher density required for global weak connectivity and with smaller integrated first- and second-dimensional Betti curves. These are dependence-aware descriptive associations: reduced-overlap estimates are imprecise, and controls for edge concentration and mean variance reduce the connectivity association to approximately zero. Direct coverage measures show that high-intensity windows can distribute weight broadly while their strongest edges reach nodes unevenly. Exact circular-shift tests yield empty 5% BH-FDR backbones, so individual channels are not treated as established. Null-model, rank-stability, coefficient-field, and discretization checks delimit the fixed-density result. A common absolute-weight contrast yields positive rather than negative TETE–Betti associations, showing that the negative baseline relation is specific to the fixed-density filtration. The results distinguish interaction intensity, dispersion, strong-edge coverage, and directed path-homology organization as separate network dimensions.

This paper examines directional quantile dependence from green bonds, clean energy markets, and carbon markets to the return states of five literature-classified green cryptocurrencies. Using daily returns from 25 September 2019 to 23 May 2025, we estimate static cross-quantilograms on a $19\times 19$ quantile grid at lags 1, 5, and 22, 500-observation rolling cross-quantilograms, bootstrap surface tests, and green-specificity comparisons with five cryptocurrencies that used proof-of-work (PoW) consensus throughout the comparison sample. Point estimates display heterogeneous short-run patterns in selected green-bond and carbon-market pairs, but none of the 45 forward surfaces rejects the omnibus null at the 5% level. Rolling estimates vary across windows and tail cutoffs. The largest raw green-group contrast occurs for carbon quota prices at lag five, although its time-series bootstrap interval includes zero and factor-adjusted tests do not detect systematic green-minus-PoW separation. Descriptive quantile-on-quantile connectedness estimates are higher at extreme quantiles than at the median–median state. Overall, the evidence is more consistent with broad cryptocurrency-market conditions than with a uniform dependence pattern associated with the environmental label.