The Bucharest University of Economic Studies (Romanian: Academia de Studii Economice din București, abbreviated ASE) is a public university in Bucharest, Romania. Founded in 1913 as the Academy of Higher-level Commercial and Industrial Studies (Academia de Înalte Studii Comerciale și Industriale (AISCI)), it has become one of the largest economic higher education institutes in both Romania and South-Eastern Europe. The Bucharest Academy of Economic Studies is classified as an advanced research and education university by the Ministry of Education. It is one of the five members of the Universitaria Consortium (the group of elite Romanian universities).
Generative artificial intelligence (GenAI) is increasingly recognized as a transformative technology that is reshaping organizational processes, individual work practices, and workplace interactions. While its benefits for efficiency and productivity are widely acknowledged, its impact on employee well-being remains largely underexplored. This study investigates the relationship between GenAI adoption and three dimensions of employee well-being: emotional, social, and cognitive. Drawing on the job demands-resources (JD-R) model and social cognitive theory, we propose a conceptual framework in which the GenAI intensity of adoption mediates the relationship between employees’ attitudes toward the technology and their well-being. By analyzing survey data from approximately 130 knowledge workers and analyzing it through partial least squares structural equation modeling (PLS-SEM), our findings reveal that a positive attitude toward GenAI significantly enhances its adoption, whereas a negative attitude does not necessarily prevent usage. Furthermore, the extent of GenAI adoption influences all three dimensions of well-being, with team cohesion acting as a mediating factor. These results contribute to the literature on workplace well-being and technology adoption by offering theoretical and managerial insights into the complex relationship between AI integration and employee experience.
This study introduces LLM-VaR and LLM-ES, novel risk estimation metrics that utilize general-purpose large language models (LLMs) for the forecasting tasks of Value at Risk (VaR) and Expected Shortfall (ES) in a zero-shot setting. Building on the input encoding mechanism of the LLMTime framework, we extend its application by defining new financial risk measures and performing an empirical evaluation of three generations of GPT models, GPT-3.5, GPT-4 and GPT-4o, versus advanced benchmark models such as GARCH with Student innovations and EWMA with Dynamic Conditional Score (DCS).Financial time series are encoded as numerical strings, allowing for model-free inference without requiring retraining. Results show that LLMs perform well when short rolling windows are used, particularly in volatile markets like cryptocurrencies. GPT-3.5 frequently outperforms or matches the performance of newer models, raising questions about model complexity, alignment, and biases. In contrast, performance deteriorates with longer windows, where the econometric models prove more reliable. Our findings demonstrate the potential of general-purpose LLMs as adaptive tools for short-horizon financial risk assessment and contribute a first-of-its-kind benchmark for LLM-based VaR/ES estimation.
The objective of this paper is to analyze time-varying spillover between bubbles in oil and stock markets of the U.S. In this regard, we first use the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to detect both positive and negative bubbles in the short-, medium and long-term in the two markets. In the second-step, we utilize a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model to conduct the spillover analysis among the indexes of oil and stock positive and negative bubbles. Based on data covering the monthly period of January 1999 to June 2025, we find that negative bubble spillovers are significantly stronger and more directional than positive ones, with the U.S. equity market emerging as the transmitter to the oil market post-2008. This represents a structural shift from the traditional oil-to-equity transmission paradigm. Moreover, spillover effects are most pronounced at short- and medium-term horizons, intensifying during crisis periods. Our findings suggest that oil is increasingly behaving as a financial asset rather than a physical commodity, with important implications for portfolio diversification and risk management.
The 2022 energy crisis exposed deep structural vulnerabilities in the European gas market, particularly in Central and Eastern Europe (CEE), a region historically dependent on Russian gas and lacking fully liberalized markets. This study provides the first comprehensive empirical assessment of the integration of the gas spot market in nine CEE countries from 2021 to 2024, employing Granger causality, Johansen cointegration, vector error correction models (VECM), impulse response functions, and forecast error variance decomposition. The results reveal an increase in regional price convergence, driven by expanded infrastructure, flexible trading mechanisms, and policy interventions such as the Ukrainian short-haul transit service and the EU's REPowerEU plan. However, integration and market cohesion remain uneven. Countries like Germany, Poland, and Czechia function as central price-setters, while others, notably Romania and Bulgaria, exhibit greater exposure to shocks. Infrastructure projects such as the GIPL interconnector and new liquefied natural gas (LNG) terminals significantly reduce price imbalances, but events like Germany's cross-border transport levy (GSU) introduce persistent distortions. This paper highlights progress and persistent asymmetries in the CEE gas market integration, offering insider policy recommendations. It demonstrates the need for coordinated policy action, harmonized regulation, and continued investment in interconnectors and storage to ensure resilient and interconnected gas markets throughout the EU.
Conventional methods for assessing competitiveness capture a limited reflection of market adaptability. This limitation needs to be addressed in light of the volatile geopolitical context and shifting trade policies, which shape an environment in which resilience is a fundamental component of competitiveness. This paper contributes to the literature by introducing tradeadjusted elasticities that capture domestic market adaptability. The objective of this research extends beyond developing a novel framework to demonstrate broader implications; it argues for the urgency of aligning market adaptability with strategic resource management to reinforce competitiveness. The European Union's vitiviniculture sector was examined as a case study, with market responsiveness being evaluated through production volumes, trade flows, and unit export values. Data collected from FAOSTAT and the International Trade Centre were used to compute the elasticities. Research findings can support decision-makers in adopting policy measures to enhance competitiveness through targeted interventions that strengthen the adaptability of domestic markets to the dynamic value chain reconfigurations.
