This study analyzes long memory, multifractality and forecasting performance in coffee, Brent oil and gold futures, three international commodity markets of strategic relevance for Colombia. Daily futures prices from 2016 to 2025 were examined using logarithmic returns, descriptive statistics, rolling volatility, Hurst R/S, detrended fluctuation analysis (DFA), multifractal detrended fluctuation analysis (MF-DFA), out-of-sample forecasting models, conditional volatility models and Monte Carlo simulation. The results show heterogeneous long-memory evidence across commodities and market regimes. R/S estimates suggested persistence in the three markets, while DFA moderated this conclusion. MF-DFA confirmed multifractal behavior in all series, with gold and Brent showing wider heterogeneity than coffee. Forecasting results showed that simple models, particularly random walk and drift specifications, were difficult to outperform in one-step-ahead predictions. Monte Carlo simulations generated probabilistic price scenarios for 30-, 60- and 90-trading-day horizons, and an ex-post validation with 2026 observed prices showed that six of nine realized prices fell within the simulated P5-P95 intervals. Overall, the findings suggest that commodity futures relevant to Colombia exhibit differentiated forms of temporal complexity, risk and scenario uncertainty that cannot be fully captured by linear models or average volatility measures.

This study articulates, documents and characterizes a processed financial dataset of daily coffee, Brent oil and gold futures prices for the period 2016-2025. The database was constructed from historical closing prices obtained from Investing.com and organized in long format, so that each record corresponds to a valid daily observation of a specific commodity. Using the original series, a reproducible transformation chain was developed, including logarithmic prices, logarithmic returns, absolute returns, squared returns, 30-day annualized rolling volatility, standardized returns, base-100 price indices, temporal labels and quality-control fields. This manuscript specifically documents the dataset architecture, the information source, quotation units, data-cleaning criteria, temporal consistency and analytical reuse possibilities. The empirical characterization shows that the three markets preserve financial features such as non-Gaussian distributions, heavy tails, heterogeneous volatility and more visible dependence in additional second-moment transformations. The resource is designed to be reused in financial econometrics, econophysics, commodity risk analysis, forecasting exercises, Monte Carlo simulation, financial time-series teaching and comparative studies of agricultural, energy and precious-metal markets.

La convergencia estructural entre los mercados de capitales y la sostenibilidad ha exacerbado la necesidad de métricas comparables que faciliten la valoración de activos, la mitigación de riesgos y la asignación eficiente de inversión. No obstante, en el contexto de los mercados emergentes, la divulgación de información Ambiental, Social y de Gobernanza (ESG) se caracteriza por una marcada heterogeneidad, vacíos informativos y una volatilidad metodológica que compromete su relevancia para el análisis financiero robusto. Para mitigar esta fragmentación, el presente estudio introduce el tablero 5P (Personas, Planeta, Prosperidad, Paz y Alianzas) como una arquitectura integradora diseñada para transmutar reportes de sostenibilidad multidimensionales en un sistema de indicadores sintéticos y un índice compuesto denominado I5P-Score. La metodología se fundamenta en un proceso sistemático y replicable que integra la determinación de materialidad, la selección de indicadores Core y extendidos, y protocolos estrictos de calidad del dato. La agregación del índice se aborda mediante tres rutas de ponderación técnica: equiponderación, criterios de materialidad experta y reducción de dimensionalidad vía Análisis de Componentes Principales (ACP), incorporando además pruebas de consistencia y sensibilidad para garantizar la estabilidad temporal del modelo. Los hallazgos de esta investigación comprenden la formalización de la arquitectura del tablero, el desarrollo de guías técnicas para la gobernanza de datos y una taxonomía de clasificación por trayectorias corporativas (rezago, transición y liderazgo), validada mediante una aplicación empírica con datos simulados. En última instancia, el tablero 5P optimiza la trazabilidad metodológica y la interoperabilidad entre las unidades de finanzas, riesgos y sostenibilidad. Este enfoque permite a las instituciones reducir la dependencia de proveedores externos de ratings y evita la simplificación excesiva de los rankings descontextualizados, proporcionando una herramienta analítica superior para la toma de decisiones estratégicas en entornos de alta incertidumbre.

This study analyzes long memory, multifractality and forecasting performance in coffee, Brent oil and gold futures, three international commodity markets of strategic relevance for Colombia. Daily future prices from 2016 to 2025 were examined using logarithmic returns, descriptive statistics, rolling volatility, Hurst R/S, detrended fluctuation analysis (DFA), multifractal detrended fluctuation analysis (MF-DFA), out-of-sample forecasting models, conditional-volatility models and Monte Carlo simulation. The results show heterogeneous long-memory evidence across commodities and market regimes. R/S estimates suggested persistence in the three markets, while DFA moderated this conclusion. MF-DFA confirmed multifractal behavior in all series, with gold and Brent showing wider heterogeneity than coffee. Forecasting results showed that simple models, particularly Random Walk and drift specifications, were difficult to outperform in one-step-ahead predictions. Monte Carlo simulations generated probabilistic price scenarios for 30-, 60- and 90-trading-day horizons, and an ex-post validation with 2026 observed prices showed that six of nine realized prices fell within the simulated P5-P95 intervals. Overall, the findings suggest that commodity futures relevant to Colombia exhibit differentiated forms of temporal complexity, risk and scenario uncertainty that cannot be fully captured by linear models or average volatility measures.

This article examines volatility, tail risk, temporal dependence, scaling behavior, and simulation-based uncertainty in daily coffee, Brent oil, and gold futures series. The sample spans 4 January 2016 to 31 December 2025 for coffee (2515 logarithmic returns) and Brent oil (2581 returns), and 4 January 2016 to 30 December 2025 for gold (2187 returns). The analysis combines historical Value at Risk (VaR) and Expected Shortfall (ES), rolling volatility, dependence diagnostics, common-date robustness, detrended fluctuation analysis (DFA), multifractal diagnostics, and bootstrap-based Monte Carlo scenarios. The vendor-supplied histories were obtained from Investing.com; observable contract-family, symbol, and quotation metadata were cross-checked against official ICE and CME specifications, while the unavailable vendor rollover algorithm is treated explicitly as a measurement limitation. A synchronized sample of 2162 return dates preserves the principal risk ranking: Brent oil remains the most volatile and records the deepest 1% ES, coffee occupies an intermediate position, and gold remains the least dispersed. DFA estimates are H = 0.460 (95% block-bootstrap CI [0.430, 0.537]) for coffee, H = 0.492 [0.380, 0.569] for Brent oil, and H = 0.436 [0.423, 0.539] for gold. Because all intervals include values near 0.5 and shuffled/AAFT-surrogate benchmarks do not support robust persistence, the evidence does not justify a strong long-memory claim. Multifractal-spectrum results show greater width for Brent oil and gold than for coffee, but shuffled-series comparisons require cautious interpretation. The contribution lies in comparing agricultural, energy and precious-metal futures within a common, reproducible and robustness-tested risk-management framework rather than in proposing a new estimator.

This article examines volatility, tail risk, temporal dependence, scaling behavior and simulation-based uncertainty in daily futures price series for coffee, Brent oil and gold during 2016-2025. The empirical object is defined as a set of provider-reported historical futures series obtained from Investing.com and transformed into logarithmic prices, logarithmic returns, absolute returns, squared returns, annualized 30-day rolling volatility, standardized returns and base-100 indices. In response to the methodological limitations inherent in secondary market-data aggregation, the study does not treat the downloaded series as exchange-certified individual contract histories, nor does it infer unobserved maturity-specific rollover rules. Instead, it positions the data as a transparent financial-risk input and evaluates the consequences of source status, contract identification and calendar irregularity for empirical interpretation. The results show non-Gaussian returns, heavy tails, heterogeneous volatility and stronger dependence in squared returns than in simple returns. Brent oil records the largest tail exposure, the highest kurtosis and the deepest maximum drawdown, while gold exhibits the most visible calendar irregularity. DFA exponents remain close to 0.5, indicating that strong long-memory claims are not supported without additional robustness tests. MF-DFA curves and Monte Carlo fan charts are therefore interpreted as exploratory risk-diagnostic tools rather than confirmatory evidence of multifractality or calibrated forecasts. The article contributes to risk and financial management by offering a cautious, reproducible and empirically delimited framework for comparing commodity futures relevant to Colombia.