Kerolly Kedma Felix Do Nascimento

We examine whether daily green bond exchange-traded fund (ETF) returns can be forecast more accurately than the zero-return benchmark. Using four ETFs with data through 17 April 2026, we compare linear, penalized, boosting, feedforward, recurrent, and combination forecasts in historical out-of-sample experiments. The contribution is empirical and evaluative, not a new algorithm. Combinations occasionally reduce squared error by less than 0.3%; the gains are statistically insignificant and heterogeneous across funds. An adjusted-price comparison with four conventional bond ETFs on identical dates finds no significant difference in combination-loss gains between the groups. Hyperparameter and dependence checks do not reverse these findings. Hypothetical close-to-close trading payoffs are sensitive to transaction costs, but are not executable backtests. Overall, the tested models provide limited incremental predictive information, without establishing market efficiency or a forecasting difficulty unique to green bonds.

Which liquid ETFs actually protect investors when U.S. equities crash? This paper answers that question using daily observations from 2012-03-02 to 2026-02-27 for SPY and eight cross-asset ETFs spanning gold, oil, broad commodities, the U.S. dollar, Treasuries, real estate, high-yield credit, and investment-grade credit. Pairwise DCC-GJR-GARCH models recover time-varying correlations, and nested downside regressions identify behavior in the lower 10%, 5%, and 1% tails of SPY returns. To prevent an incremental correlation decline from being misclassified as a safe haven, inference is based on HC3-robust Wald tests of the cumulative tail-state correlations. In the full sample, the U.S. dollar and U.S. Treasuries qualify as hedges, while the U.S. dollar and U.S. Treasuries reach statistically negative correlation in at least one downside state. Protection is sharply regime-dependent. During COVID-19, effective safe-haven behavior is limited to the U.S. dollar and U.S. Treasuries; during the Russia–Ukraine regime it is observed for the U.S. dollar; and in the tariff window the hedge set narrows to gold and U.S. Treasuries. Across specifications, dollar exposure appears most frequently, while sovereign duration produces the deepest negative dependence in the full sample, COVID-19, and the tariff window. Gold remains episodic, and cyclical ETFs generally retain positive equity dependence. The findings show that hedge and safe-haven labels are state-contingent properties of the cross-asset correlation structure rather than permanent attributes of individual ETFs.

This paper examines the multifractal organization of hourly wind-speed dynamics at nine automatic stations of the Brazilian National Institute of Meteorology in Paraíba, Northeast Brazil, over the common interval from 3 November 2017 to 17 November 2019. Missing records and values outside the inclusive 1.0–18.0 m/s analytical range are removed before station-level standardization, and multifractal detrended fluctuation analysis is applied to compacted retained-observation sequences. The stability-potential index (SPI) is defined as |α₀ − 0.5|, where α₀ is the singularity-spectrum peak and 0.5 is the uncorrelated benchmark. SPI therefore measures departure from random-like scaling and is interpreted as a temporal-organization indicator, not as a stand-alone measure of wind-power resource. Rolling windows of 720 retained observations, advanced by 168 observations, are assigned to seasons, El Niño, La Niña, neutral conditions, and tropical Atlantic-gradient regimes. The empirical design combines full-sample rankings, rolling spectra, two-way clustered ordinary least squares, Frisch–Newton bootstrap quantile regression, non-overlapping and coverage-threshold sensitivities, clock-span diagnostics, analytical-range and moment-order checks, 200 shuffled surrogates per station, and station-anchored inverse-distance-weighted maps. All full-sample α₀ estimates exceed 0.5, with SPI ranging from 0.1108 in Cabaceiras to 0.3937 in Areia. El Niño is associated with lower rolling SPI in both baseline and controlled mean regressions and in the lower and median quantiles; this sign persists after excluding low-coverage Camaratuba. La Niña is not significant at the mean but is negative at the 0.90 quantile. No north-warm Atlantic-gradient windows occur in the common sample, while the south-warm gradient is positive only near the median quantile. Four of nine stations have original raw spectrum widths above the shuffled 97.5% bound. Across stations, exact two-sided Spearman tests show no supported monotonic association between coverage, wind-distribution descriptors, or altitude and α₀, SPI, or W (n = 9; all p ≥ 0.194). The results document sample-specific spatial heterogeneity and climate-regime associations while showing that compacted-window duration, calm-wind screening, and the modest nine-station network require explicit sensitivity analysis and cautious generalization.

This paper examines hourly wind speed dynamics at 12 INMET stations in Piauí, Brazil, asking whether the local multifractal organization of the wind field moves closer to or farther from a random benchmark across meteorological seasons. The observational base is reorganized into a rolling multifractal framework with windows of 720 h observations and steps of 168 h. For each full sample and rolling window, the singularity-spectrum peak is translated into a stochastic inefficiency index equal to its absolute distance from 0.5, so lower values indicate greater stochastic efficiency. The analysis combines this index with mean wind speed and with the empirical third moment of speed, [ ], proportional to mean wind power density under constant air density. Full-sample results identify PIRIPIRI, GILBUES, and TERESINA as closest to the random benchmark, whereas PARNAIBA is the most resource-rich station and the most structurally distant from that benchmark. Rolling results reveal marked seasonality: mean stochastic inefficiency is highest in autumn (0.2820) and lowest in winter (0.2488). Fixed-effects and quantile regressions show that winter and spring reduce stochastic inefficiency relative to summer, whereas autumn is statistically indistinguishable from the summer benchmark in most specifications. Overall, GILBUES, CANTO DO BURITI, OEIRAS, and CARACOL provide the most balanced combination of wind-resource magnitude and stochastic efficiency in the active 12-station subset.

This study analyzed the wind speed distribution in the state of Piauí, Brazil, aiming to estimate its wind energy generation potential. Hourly wind speed data measured at 10 m above ground level from 2010 to 2023 were analyzed. The mean wind speed ranged from 4.0 to 10.0 m s⁻¹, with higher values observed in the coastal zone and eastern region of the state. Several probability distributions (Weibull, Gamma, Lognormal, Logistic, and Normal) were fitted to the data, with the Weibull distribution showing the best performance based on the Akaike (AIC) and Bayesian (BIC) information criteria across all stations. The AIC and BIC values for the Weibull model ranged from 500 to 2000. The Weibull shape (k) and scale (c) parameters varied between 1.5 and 5.0 and 4.0–13.0 m s⁻¹, respectively. Estimated wind power density (WPD) values ranged from 50 to 400 W m⁻², indicating moderate to high wind energy potential in specific areas. Spatial analysis using inverse distance weighting (IDW) interpolation revealed favorable regions for wind farm deployment. These results provide quantitative support for sustainable energy planning and offer strategic insights for renewable energy development in Piauí.

In this study, we conducted a comprehensive analysis of the spatiotemporal variability of wind speed in the Northeast region of Brazil, based on hourly time series spanning 12 years. Information-theoretic complexity measures, such as Shannon entropy and Fisher information, were applied to the wind speed data. The inverse distance weighting (IDW) interpolation method was employed to estimate wind behavior in unmonitored locations. The time series were evaluated in the Fisher information–Shannon entropy plane, allowing the identification of stations exhibiting more stochastic behavior and, consequently, greater potential for wind energy generation. In this context, we introduced a novel indicator to assess wind quality for energy production purposes, namely, the Stochastic Efficiency. The results indicate that a substantial portion of the region presents significant wind energy potential, especially in coastal and semi-arid areas. Stations with higher entropy and lower Fisher information, such as Serra Talhada–PE, Piatã–BA, and Colinas–MA, exhibited higher stochastic efficiency and unpredictability — an advantageous trait for wind power generation. Conversely, stations such as Maceió–AL and Aracati–CE showed greater informational structure — lower stochastic efficiency and unpredictability — indicating more regular wind patterns, which may be less suitable for certain renewable applications. This approach, applied here for the first time, proved robust for characterizing wind resources and ranking meteorological stations in terms of their suitability for clean energy production. The findings provide valuable insights for energy planning, strategic site selection for wind farms, and the improvement of statistical and predictive modeling in renewable energy contexts. The study also highlights the growing importance of the states of Bahia, Rio Grande do Norte, Piauí, and Ceará in the national energy matrix, solidifying the Northeast as Brazil’s leading wind energy-producing region.

Climate change has driven the market to seek new ways of raising funds to mitigate its effects. One such innovation is the emergence of Green Bonds financial assets specifically designed to support sustainable projects. This study explores the fractal behavior of daily price changes in thirty-five Green Bond funds using the Multifractal Detrended Fluctuation Analysis (MFDFA) method. Our results indicate that price changes exhibit persistent behavior and high multifractality, characterized by large fluctuations. Only one of the thirty-five time series analyzed showed an outlier result, suggesting that the funds display very similar behavior. By shuffling the series, we were able to reduce multifractality significantly. These findings suggest that Green Bond funds exhibit multifractal behavior typical of other financial assets.