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.