Publications

This study analyzes the dynamic interdependence between tourism activity, energy transition variables, and macroeconomic uncertainty in Spain from 2019 to 2024. Using a Quantile Connectedness Approach based on a Quantile Vector Autoregression (QVAR) model, it captures asymmetric and state-dependent spillovers among tourism emissions, jet fuel and carbon prices, inflation, and uncertainty indicators. Results show that the tourism–energy nexus forms a highly interconnected system whose intensity varies across market regimes. During crises, energy and uncertainty variables dominate as shock transmitters, while in recovery phases, air tourism regains influence, supporting system reactivation. Jet fuel and carbon prices are key transmission channels linking tourism and macroeconomic volatility, with inflation and policy uncertainty amplifying effects. Carbon pricing evolves from a volatility source to a stabilizing mechanism as transition expectations consolidate. The findings highlight asymmetric resilience in tourism and provide insights into policies aligning tourism recovery with energy transition and climate goals.

This study analyzes the interactions between various sources of energy generation and climate variables in Colombia, using Vector Autoregressive Models (VAR) and Generalized Additive Models (GAM). The main objective is to understand the dynamics and complementarity between fossil, hydroelectric, solar and wind energy sources, as well as their relationship with critical climate factors. The results of the VAR model reveal a strong interdependence between fossil and hydroelectric generation. Impulse-response functions show that a shock to fossil generation has a significant and sustained impact on hydropower generation and vice versa, highlighting the role of fossil generation as a backup during periods of water stress. Additionally, patterns of interdependence are observed between fossil generation and renewable sources, especially solar, which is beginning to have a more notable impact on fossil generation. GAM analysis complements these findings by providing a nonlinear perspective on the relationships between variables. Hydroelectric generation is found to be strongly influenced by fossil generation, with a significant negative relationship, suggesting that as hydroelectric generation increases, the need for fossil generation decreases. Furthermore, climatic variables such as solar radiation and water reserves have significant relationships with hydroelectric generation, underlining the dependence of the energy system on climatic conditions. Colombia's energy matrix depends 70 % on hydroelectric generation, which, although sustainable under normal conditions, can become critical during periods of drought. In such scenarios, fossil generation becomes an essential backup. The incorporation of non-conventional renewable energy sources, such as solar and wind, is crucial to diversify the energy matrix and improve the resilience of the system.

Electricity tariffs in emerging economies are often influenced by macroeconomic volatility and regulatory design, affecting both affordability and system stability. Understanding these interactions is crucial for anticipating price fluctuations and ensuring sustainable energy policy. This paper examines the influence of macroeconomic conditions on electricity tariff dynamics in Colombia by integrating econometric and machine learning approaches. Using monthly data from 2009 to 2024 and a set of 153 macroeconomic indicators condensed via principal component analysis (PCA), we assess the predictive performance of vector autoregressive (VAR), SARIMAX, and XGBoost models, as well as a hybrid VAR–XGBoost specification. Impulse-response analysis reveals that tariff components exhibit limited sensitivity to macroeconomic shocks, underscoring the buffering role of regulation and sector-specific drivers. However, forecasting exercises demonstrate that accuracy is highly component-specific: SARIMAX performs best for transmission and restrictions, and VAR dominates for distribution and losses, while the hybrid model outperforms for generation and commercialization. These findings highlight that although macroeconomic pass-through into tariffs is weak, hybrid approaches that combine structural econometric dynamics with nonlinear learning can deliver tangible forecasting gains. The study contributes to the literature on electricity pricing in emerging economies and offers practical insights for regulators and policymakers concerned with tariff predictability and energy affordability.

This study assesses energy productivity changes in 26 European countries from 2005 to 2019 using a Metafrontier Malmquist-Luenberger Productivity Index (MML), incorporating both desirable outputs and undesirable outputs. By distinguishing between European regions, the analysis reveals structural heterogeneity in energy transitions. The inclusion of energy poverty offers a more equitable framework to evaluate not only technical efficiency but also social performance. Results show mixed productivity trends: while some countries achieve efficiency and technological gains, others display stagnation or regression when equity dimensions are included. Eastern Europe, despite lower average productivity under the joint orientation (MML = 0.9899), demonstrates improvement when the focus shifts to energy poverty (MML = 1.0351), suggesting that equity-focused policies can yield meaningful outcomes even in less efficient systems. In contrast, Western Europe shows relatively lower productivity under the baseline joint orientation (MML = 0.9049), with only a slight improvement when energy poverty is prioritized (MML = 0.9164), highlighting persistent challenges in addressing distributive aspects despite stronger technological capabilities. Overall, the findings underscore the importance of integrating equity considerations into energy efficiency assessments, showing that technological progress alone does not guarantee socially inclusive energy transitions.

This study investigates the complex relationship between armed conflicts and electricity prices in Colombia, considering the effects of climatic variables. Given the strategic importance of energy resources for economic and social development, understanding how conflicts influence energy prices is crucial. Using Generalized Additive Models (GAM) and Distributed Lag Non-linear Models (DLNM), we analyze the immediate and delayed impacts of battles, deaths, solar radiation, and precipitation on electricity prices. Our findings reveal that battles significantly increase electricity prices contemporaneously, highlighting the vulnerability of Colombia's electrical infrastructure. The DLNM analysis shows that these effects can reemerge weeks after the initial conflict, with significant price increases particularly between weeks four to six and seven to eight. Additionally, climatic variables like solar radiation and precipitation exhibit non-linear effects on electricity prices, where moderate increases in these variables reduce prices, but extreme conditions elevate them. These results underscore the need for integrated strategies that address both socio-political and climatic factors to enhance energy resilience. Our study provides valuable insights for policymakers and energy sector stakeholders, emphasizing the importance of mitigating conflict impacts and adapting to climatic variability to ensure a stable and sustainable electricity supply in Colombia.

This study examines the impact of macroeconomic factors on the financial performance of power generation firms, comparing emerging Latin American economies with those of a developed market. Based on panel data from 106 companies across six countries (2018–2022), multilevel modeling is used to assess firm- and country-level effects, complemented by Dual Multiple Factor Analysis (DMFA) to identify latent relationships among financial and macroeconomic variables. The results indicate that exchange rate fluctuations and inflation are significant determinants of return on equity (ROE), with coefficients of 0.57 and −0.32, respectively. Internal indicators, such as gross profit margin (β = 0.30) and quick ratio (β = 0.22), also exhibit strong positive associations with ROE. Differences in macroeconomic sensitivity between developed and emerging markets underscore the importance of context-specific financial strategies in the energy sector.

This study presents a comprehensive bibliometric and topic modeling analysis of the academic literature on green and sustainable finance. Using 1372 peer-reviewed articles indexed in the Web of Science up to 2024, we identify key publication trends, influential authors, prominent journals, and thematic clusters shaping the field. The analysis reveals an exponential growth in publications since 2017 and highlights the dominance of journals such as Journal of Sustainable Finance and Investment and Sustainability. Text mining techniques, including TF-IDF and Latent Dirichlet Allocation (LDA), are applied to abstracts to extract the most relevant terms and classify articles into four latent topics. The findings suggest a growing focus on the impact of green finance on carbon emissions, energy efficiency, and firm performance, particularly in the context of China. This study offers valuable insights for researchers and policymakers by mapping the intellectual structure and identifying emerging research frontiers in the rapidly evolving field of green finance.

This study examines the dynamic and nonlinear effects of global interest rate (based on the G7 market) shocks on equity markets in BRICS countries. A World Interest Rate (WIR) index is constructed using principal component analysis of short-term interest rates from developed economies. The analysis applies Distributed Lag Nonlinear Models (DLNMs) to evaluate the temporal response of each market to positive and negative WIR shocks over a six-period horizon. The results reveal notable asymmetries and heterogeneity. Brazil and Russia experience stronger reactions to negative shocks, while India and China show milder or delayed effects. South Africa stands out for its persistent and symmetric sensitivity to both types of shocks, suggesting deeper exposure to global financial cycles. The DLNM framework allows for a nuanced interpretation of exposure-lag relationships, offering new insights into how global monetary conditions affect emerging markets. These findings highlight that financial integration does not imply uniform vulnerability across countries and that global liquidity shocks can trigger diverse equity market responses. This paper contributes to the literature on international financial linkages and provides relevant implications for investors and policymakers managing portfolio exposure or economic risk in emerging markets.

Research on portfolio optimization for energy generation often does so from a financial perspective. This study addressed a unique challenge, determining which companies, amidst a globalized electricity market, should be retained for climate risk preservation during specialization. Utilizing weather and generation data from 106 power plants across Argentina, we adapted integer portfolio optimization tools. Originally designed for financial index funds, these tools helped us construct a portfolio of power plants for a resilient energy mix. Our findings revealed optimal companies for retention by analyzing different portfolio configurations, where the number of plants was adjusted iteratively. In each iteration of the model, we selected a set of representative plants that minimize climate risk, which sometimes resulted in a plant being included in one portfolio but not another. This approach identified the specific companies and technologies essential for a diversified and climate-resilient energy portfolio while ensuring a strategic transition toward specialization and stabilizing generation risk in the face of variable weather conditions. This paper presents a groundbreaking solution for specialization in a globalized energy market. Through portfolio optimization, we identified pivotal companies for each stage of the transition in Argentina. Firms like Parque Eólico La Genoveva and Complejo Hidroeléctrico Centrales Cacheuta Alvarez Condarco, showcased the balance needed for wind and hydroelectric sources. These insights should be used to guide policymakers to ensure a controlled and effective transition while maintaining stable generation risk.

This study examines how sovereign risk shapes the financial performance of sustainable investments, using the MSCI Emerging Markets ESG Index as a reference. The analysis covers 24 emerging and frontier economies from Latin America, Asia, the Middle East, and Eastern Europe during 2016–2025, a period marked by major global disruptions such as the COVID-19 crisis and post-2022 financial tightening. Sovereign risk dimensions are extracted through Principal Component Analysis (PCA) applied to sovereign CDS spreads, identifying a systemic component linked to global shocks and a structural component associated with domestic fundamentals and governance quality. These factors are integrated into a quantile regression framework alongside control variables—oil prices, interest rates, and global equity indices—capturing key macro-financial transmission channels. Results show a nonlinear, quantile-dependent relationship: systemic risk intensifies ESG losses under adverse conditions, while structural improvements support gains in upper quantiles. Control variables behave as expected, confirming the macro-financial sensitivity of ESG performance. The findings reveal that ESG returns are state-dependent and strongly influenced by sovereign credit dynamics, especially in emerging markets where external shocks and institutional fragility intersect. Strengthening sovereign governance and integrating risk diagnostics into ESG assessments are essential steps to enhance resilience and credibility in sustainable finance.

The increasing complexity of energy systems amid the global push for decarbonization raises important questions about how transitions in the energy matrix affect financial risk in electricity markets. This study investigates the relationship between structural changes in national energy matrices and the systemic risk associated with electricity prices in Europe from 2015 to 2022. Using daily electricity price data, we calculate log returns and estimate the Value at Risk (VaR) at the 1% level as a measure of extreme financial loss. We incorporate energy market variables, including the volatility of Brent oil and coal prices, and an entropy-based indicator derived from the Shannon index, which captures the degree of technological dispersion in the energy mix over time. A fixed-effects panel regression model is applied across 21 European countries to identify the drivers of energy-related financial risk. Results show that higher volatility in Brent and coal prices significantly increases the VaR, and that greater entropy reflecting a more complex and dynamic energy transition also correlates with higher systemic risk. These findings suggest that while energy diversification is a goal of sustainability, it may entail short-term instability. The study contributes to the understanding of how structural transformations in energy systems interact with financial vulnerabilities in liberalized electricity markets.

The extent to which external factors explain profitability in the energy sector and their commonalities is largely unknown from the previous literature. We identify three latent factors underlying the profitability of 1,347 global energy firms, from 2000 Q1 to 2021 Q2. We rely on a novel Dynamic Factor Model estimated by Functional Principal Components. Profitability factors are strongly associated with global financial and macroeconomic factors, including energy commodity prices, interest rates, exchange rates, economic activity and financial uncertainty. We compare our empirical results for various energy subsectors and show that profitability of oil and gas companies is highly sensitive to changes in interest rates and fuel prices, while renewable energy and uranium firms are more sensitive to exchange rates. We also provide a ranking of firms based on their association with the common factors of profitability, which can be used to monitor the resilience of the energy sector.

This study aims to explore the asymmetric effects of macroeconomic factors on the profitability of large-cap companies in an emerging country like Colombia, using the Morgan Stanley Capital International (MSCI) Colombia index as the basis. We employ a combination of singular spectrum analysis (SSA) and principal component analysis (PCA) to identify and estimate four key macroeconomic factors that account for approximately 47.8% of Colombia's macroeconomy. These factors encompass indicators related to inflation and cost of living, foreign trade and exchange rate, employment and labor force and trade and production in Colombia. We utilize the distributed lag nonlinear model (DLNM) to analyze the asymmetric relationships between these factors and corporate profitability, considering different scenarios and lags. Our analysis reveals that there are indeed asymmetric relationships between the identified macroeconomic factors and corporate profitability. These relationships exhibit variability over time and lags, indicating the nuanced nature of their impact on corporate performance. This study contributes to the existing literature by applying a novel methodology that combines SSA and PCA to identify macroeconomic factors within the Colombian context. Additionally, our focus on asymmetric relationships and their dynamic nature in relation to corporate profitability, using DLNM, adds original insights to the research on this subject.

This study explores the macrofinancial interconnectedness within the Pacific Alliance (Chile, Colombia, Mexico, and Peru) by utilizing a Quantile Vector Autoregression (QVAR) approach to dissect the directional spillover effects between macroeconomic variables and MSCI stock indices. By employing autoencoder techniques for dimensionality reduction, the research decodes significant macroeconomic dimensions influencing stock market behaviours. The findings show patterns of spillover effects, highlighting the variable roles of these economies as transmitters and receivers of economic shocks, particularly after the Pacific Alliance agreement.

This study examines how global liquidity influences the profitability of companies in the energy sector using a non-linear approach. Applying Functional Principal Component Analysis (FPCA) to sparse data, we reconstruct the profitability history of 497 energy companies in various subsectors, including coal, oil and gas, oil and gas-related equipment and services, renewable energy, and uranium. We use a Distributed Delay Nonlinear Model (DLNM) to estimate the impact of global liquidity on profitability. Our results reveal distinct nonlinear patterns in the response of these subsectors to changes in global liquidity. For example, in the oil and gas subsector, an extreme quantile (99%) of global liquidity is associated with a significant 5.19% increase in profitability in the first quarter after the shock. In contrast, in the renewable energy subsector, a lower quantile (25%), corresponding to a moderately downward trend, is associated with a 0.73% increase in profitability over the same period. These finings are crucial to improving risk management, investment and policy development strategies within the energy sector, offering a deeper understanding of the dynamics in the global financial and economic landscape.

We analyze the impact of temperature, wind speed, solar radiation, and precipitation on wholesale electricity prices in six European countries, considering the full distribution of these weather variables. Our findings show nonlinear and extreme weather effects on electricity prices. For instance, lower temperatures raise electricity prices in all countries, with colder countries having lower thresholds than warmer ones. Warmer countries also have an upper temperature threshold above which prices rise. The precipitation threshold is higher in countries with limited hydroelectric capacity and lower in those with high hydropower, like Norway. Wind speed consistently affects electricity prices across all countries, while solar irradiance is significant in countries with high solar capacity. Overall, the impact of weather on electricity prices depends on a country’s climate, energy mix, policies, efficiency levels, and behavioral factors. Effective policies to mitigate the adverse effects of climate change should be informed by a precise understanding of these impacts.

This paper examines the relationship between the World Interest Rate (WIR) and the Stoxx 600 Banks index (SX7P) using a quantile approach and cross-quantilogram. The results reveal that the Stoxx 600 Banks index strongly predicts the WIR when its index is low, and the WIR significantly influences the SX7P when it is high. The study demonstrates that during a global crisis, the Stoxx 600 Banks index receives shocks, while the WIR acts as a transmitter. The study provides valuable insights into the cyclical pattern and complex relationship between WIR and SX7P, benefiting policymakers, investors, and financial analysts.

This research identifies the significant relationships between climate and macroeconomic variables with the financial profitability (ROA) of energy sector companies in Germany, Norway, France and Spain. We work under the hypothesis of the existence of non-linear relationships for which we fit a Generalized Additive Model (GAM) for each country. We find that macroeconomic variables are often considered more important for modeling profitability than climate variables. This is because general economic conditions, such as interest rates and commodity prices, can have a broader and deeper impact on a firm’s financial performance than local climate variations. However, climatic conditions are relevant if the specific industry consists of renewable energy companies. The results of this study can be very useful for financial analysts and investors, as they can adjust their business strategies to improve their financial performance.

Various analyses have studied the hypothesis of the change in mortality rates, finding, in some cases, a decrease, which has been associated to different factors, including economic growth. This study applies a cross-quantilogram to study the relationship between GDP per capita and the mortality rate for both men and women, taking the United Kingdom as a case study. The objective is to show that there are associations between different quantiles of the variables studied. It is found that there are asymmetric associations, the results show that there is a greater impact of GDP (Gross domestic product) per capita on the mortality rate, compared to the opposite relationship. In the case of women and men, high quantiles of economic growth have a greater impact on reducing mortality rates compared to low quantiles of economic growth, this may be a factor that can be attributed to the highly loaded labor force for males.

Assets and Liabilities Management (ALM) has been a topic of interest for decision makers in the private and public sectors. This study shows a systematic review of 416 published research paper, in the Scopus citation index, associated with ALM in a period of time from 1983 to 2021. The main objective is to identify the lines of publication over time, emphasize the journals most associated with the subject, as well as the relevant authors. The tool that was used was Bibliometrix of R, which allowed to identify the bibliometric indicators in the publications. In addition, the work proposes a modeling of themes based on text mining methodologies. The results allow us to conclude that quantitative research has been widely exploited through the 6 themes, Financial risk management, Stochastic modeling, Pension funds, Capital markets and the insurance industry, Mean-variance models, General optimization models. This research allows us to identify gaps in the literature to frame future research.

We study banks’ profitability in the US economy by means of dynamic factor models. Our results emphasize the importance of a few common cyclical market factors that greatly determine banking profitability. We conduct exhaustive regressions in a big data set of macroeconomic variables aiming to gain interpretability of our statistical factors. This allows us to identify three main macroeconomic factors underlying banking profitability the financial burden of households and economic activity; household income and net worth and, in the case of ROA and ROE, stress in financial markets. We also provide an integrated perspective to analyse banks’ profitability dynamically and to inform policymakers concerned with financial stability issues, for which banks’ profitability is fundamental. Our models allow us to provide several rankings of vulnerable financial institutions considering the common market forces that we estimate. We emphasize the usefulness of such an exercise as a market-monitoring tool.

Stunting (low height for age) is a crucial indicator for measuring child well-being and economic and social development of a country. Despite a decrease in overall children’s stunting in the last decades, there are still significant geographic disparities between urban and rural areas in Colombia. This paper aims to identify the role of the main determinants of children’s stunting in explaining the urban-rural stunting gap. We use data from the 2015 National Nutritional Situation Survey (the most recent available dataset) and the Yun’s statistical decomposition technique. We find that the urban-rural gap in child stunting is 7.2 percentage points. Three determinants household wealth, maternal education, and health services utilization, explain most of the gap (92%). Each determinant explains 54%, 26%, and 12% of the characteristics effect, respectively. Public health policies aiming to reduce the gap must seek improvements in access to institutional delivery and education services for mothers in rural areas in the short term. In the long term, increasing economic wealth in rural areas is essential.

Breeding cassava relies on several selection stages (single row trial-SRT; preliminary; advanced; and uniform yield trials—UYT). This study uses data from 14 years of evaluations. From more than 20,000 genotypes initially evaluated only 114 reached the last stage. The objective was to assess how the data at SRT could be used to predict the probabilities of genotypes reaching the UYT. Phenotypic data from each genotype at SRT was integrated into the selection index (SIN) used by the cassava breeding program. Average SIN from all the progenies derived from each progenitor was then obtained. Average SIN is an approximation of the breeding value of each progenitor. Data clearly suggested that some genotypes were better progenitors than others (e.g., high number of their progenies reaching the UYT), suggesting important variation in breeding values of progenitors. However, regression of average SIN of each parental genotype on the number of their respective progenies reaching UYT resulted in a negligible coefficient of determination (r2 = 0.05). Breeding value (e.g., average SIN) at SRT was not efficient predicting which genotypes were more likely to reach the UYT stage. Number of families and progenies derived from a given progenitor were more efficient predicting the probabilities of the progeny from a given parent reaching the UYT stage. Large within-family genetic variation tends to mask the true breeding value of each progenitor. The use of partially inbred progenitors (e.g., S1 or S2 genotypes) would reduce the within-family genetic variation thus making the assessment of breeding value more accurate. Moreover, partial inbreeding of progenitors can improve the breeding value of the original (S0) parental material and sharply accelerate genetic gains. For instance, homozygous S1 genotypes for the dominant resistance to cassava mosaic disease (CMD) could be generated and selected. All gametes from these selected S1 genotypes would carry the desirable allele and 100% of their progenies would be resistant. Only half the gametes produced by the heterozygous S0 progenitor would carry the allele of interest. For other characteristics, progenies from the S1 genotypes should be, at worst, similar to those generated by the S0 progenitors.

Breeding cassava relies on a phenotypic recurrent selection that takes advantage of the vegetative propagation of this crop. Successive stages of selection (single row trial–SRT; preliminary yield trial–PYT; advanced yield trial–AYT; and uniform yield trials UYT), gradually reduce the number of genotypes as the plot size, number of replications and locations increase. An important feature of this scheme is that, because of the clonal, reproduction of cassava, the same identical genotypes are evaluated throughout these four successive stages of selection. For this study data, from 14 years (more than 30,000 data points) of evaluation in a sub-humid tropical environment was consolidated for a meta-analysis. Correlation coefficients for fresh root yield (FRY), dry matter content (DMC), harvest index (HIN), and plant type score (PTS) along the different stages of selection were estimated. DMC and PTS measured in different trials showed the highest correlation coefficients, indicating a relatively good repeatability. HIN had an intermediate repeatability, whereas FRY had the lowest value. The association between HIN and FRY was lower than expected, suggesting that HIN in early stages was not reliable as indirect selection for FRY in later stages. There was a consistent decrease in the average performance of clones grown in PYTs compared with the earlier evaluation of the same genotypes at SRTs. A feasible explanation for this trend is the impact of the environment on the physiological and nutritional status of the planting material and/or epigenetic effects. The usefulness of HIN is questioned. Measuring this variable takes considerable efforts at harvest time. DMC and FRY showed a weak positive association in SRT (r = 0.21) but a clearly negative one at UYT (r = −0.42). The change in the relationship between these variables is the result of selection. In later stages of selection, the plant is forced to maximize productivity on a dry weight basis either by maximizing FRY or DMC, but not both. Alternatively, the plant may achieve high dry root yield by simultaneously attaining “acceptable” (but not maximum) levels of FRY and DMC.