GIUV2026-019

In an economic and financial context marked by structural uncertainty, non-linear dynamics, and increasing computational demands, the group focuses on the development of advanced quantitative methods for decision-making under risk. Its research combines tools from applied economics, optimization, and financial mathematics with emerging paradigms such as quantum computing, hybrid classical-quantum algorithms, and artificial intelligence. The group places particular emphasis on portfolio optimization, efficient allocation of scarce resources, and financial and systemic risk modelling, addressing large-scale problems that exceed the capabilities of conventional analytical and numerical approaches. Beyond methodological contributions, the research aims to generate transferable knowledge with direct applicability to economic analysis, business decision-making, and regulatory and supervisory frameworks.

https://www.uv.es/uvweb/universidad/es/ficha-persona-1285950309813.html?p2=vicferpa&idA=

• To develop advanced quantitative methods based on quantum and hybrid algorithmics for portfolio optimization and efficient asset allocation under high uncertainty.
• To implement and assess error-mitigation techniques and hybrid strategies on noisy intermediate-scale quantum (NISQ) architectures, ensuring the robustness and reliability of economic and financial models
• To develop quantitative solutions for the pricing of complex financial assets and advanced risk measurement (VaR, CVA, stress testing), integrating quantum computing, stochastic simulation, and mathematical optimization.
• To promote knowledge and technology transfer to the productive sector through the development of specialized software, decision-support tools, and collaborations with firms and institutions in the financial sector.
• To develop and apply advanced mathematical and econometric models, integrating artificial intelligence and machine learning with classical quantitative methods, for the analysis of complex economic systems, market dynamics forecasting, and decision support in applied economics and business management.
• To develop and analyze advanced ecological inference and computational statistics methods, incorporating quantum computing and hybrid algorithmics, for the study of aggregated electoral behavior and the analysis of electronic voting (e-voting) systems, with the aim of improving inferential accuracy, model robustness, and the assessment of security and transparency in democratic processes.

• Quantum Finance and Hybrid Algorithmics.Development and application of advanced quantitative methods for economic and financial optimization through variational quantum computing and hybrid classical¿quantum algorithms. This line focuses on portfolio optimization, derivative pricing, stochastic simulation, and risk management under uncertainty, exploring the potential of NISQ architectures and error-mitigation techniques to enhance computational efficiency and solution quality in complex financial environments.
• Operations Research, Applied Mathematics and Artificial Intelligence.Research on advanced mathematical models, operations research, and optimization under uncertainty, integrating artificial intelligence and quantum computing as computational and algorithmic acceleration tools. This line focuses on efficient resource allocation, stochastic programming, robust optimization, and decision support in applied economics and business management.
• Risk Modeling, Advanced Econometrics and Quantitative Regulation.Development of advanced econometric and mathematical models for the measurement, management, and simulation of economic and financial risk, integrating artificial intelligence and quantum computing for complex scenario analysis. This line includes applications in VaR, CVA, stress testing, and quantitative support for regulatory and supervisory frameworks.
• Ecological Inference, Computational Political Economy and E-Voting.Research on ecological inference, computational statistics, and mathematical modeling applied to the analysis of aggregated collective behavior and democratic processes. This line integrates artificial intelligence and quantum computing to enhance inferential accuracy, computational scalability, and model robustness, with applications in electoral studies, applied political economy, and the assessment of electronic voting (e-voting) systems.
• Decision Support Systems and Technology Transfer in Digital Economy.Design of decision support systems based on mathematical models, artificial intelligence, and quantum computing, aimed at transferring research results to the real economy. This line promotes the development of scientific software, advanced analytical tools, and applied solutions in economics, finance, governance, and digital public services.

• Applied Economics

• QUANTITATIVE METHODS
• MATHEMATICS
• STATISTICS
• QUANTUM ECONOMICS
• APPLIED ECONOMICS
• QUANTUM FINANCE
• RISK MANAGEMENT
• PORTFOLIO OPTIMIZATION
• QUANTUM COMPUTING