جست‌وجوی استاد و پژوهشگر

Şükriye Tüysüzمشاهده پروفایل

استاد

• Financial Econometrics
• Risk Management
• Derivative Markets
• +۳ مورد دیگر

Şükriye Tüysüz is a Professor at Yeditepe University's Faculty of Economics and Administrative Sciences, Department of International Finance. She has held progressive academic positions at Yeditepe since 2012, advancing from Assistant Professor to Professor (2022). Previously, she taught at French institutions including Ecole Supérieure des Sciences Commerciales d'Angers and Université Strasbourg I. Education: PhD, Académie de Strasbourg (2001-2007) Integrated PhD, Université Panthéon-Assas: Paris II (1997-1998) Bachelor, Académie de Strasbourg (1993-1997) Her research focuses on financial econometrics, risk modeling, derivatives markets, and monetary policy impacts. She specializes in advanced quantitative methods including wavelet analysis, conditional correlations, and transition probability matrices applied to international financial markets. Publications primarily explore financial risk measurement techniques (VaR, ES, LGD), market interdependencies during crises, and monetary policy transmission. Recent works investigate predictive modeling of stock volatility, sovereign risk in emerging markets, and unconventional data sources like social media. Administrative service includes Board Membership at Yeditepe University (2020) and student affairs coordination. She teaches undergraduate and graduate courses in Derivatives, Risk Management, Financial Econometrics, and Statistics.

• Computational Intelligence
• Machine Learning
• Time Series Forecasting
• +۵ مورد دیگر

Dr. Hai Yan Lu is an Associate Professor at the School of Computer Science , Faculty of Engineering and Information Technology , University of Technology Sydney (UTS) . She serves as Head of Discipline for Data Analytics and is a core member of the Decision Systems and e-Service Intelligence Lab within the Centre for Artificial Intelligence at UTS. Her academic journey includes a PhD in Engineering (2002) from UTS, Master of Engineering (1988) and Bachelor of Engineering (1985) from Harbin University of Science and Technology , China. She transitioned from a Lecturer at Yanshan University to UTS in 1994. Research Interests: Computational Intelligence, Time Series Forecasting, Data Analytics, Electromagnetic Material Modeling, 3D Human Pose Estimation, Smart Energy Systems Key Projects: Smart Power Grids, Intelligent Multi-Agent Systems, Predictive Analytics for Renewable Energy, Electromagnetic Device Optimization Technical Expertise: Machine Learning, Deep Learning, Federated Learning, Multi-Objective Optimization, Granular Computing Professional Affiliations: Senior Member, IEEE Member, ACM Vice Chair, IEEE NSW Section Women in Engineering Secretary/Treasurer, IEEE NSW Computational Intelligence Chapter Her recent publications demonstrate cutting-edge work in: Regularized Deep Learning for Financial Risk Modeling GAN-based Environmental Forecasting Hybrid Forecasting Systems for Solar Energy Advanced Magnetic Material Characterization Explainable AI for Industrial Applications

• Risk management
• Systemic risk
• Value-at-Risk (VaR)
• +۶ مورد دیگر

Christophe Pérignon is a Professor and Associate Dean for Research at HEC Paris, a leading business school in France. He is affiliated with the Finance Department and has established himself as a prominent scholar in financial risk management, systemic risk, and computational reproducibility in finance. His work bridges academic research with practical applications in banking regulation and risk management. His research spans several critical areas in modern finance: Advanced risk management methodologies, particularly Value-at-Risk (VaR) modeling and validation Systemic risk measurement and financial regulation frameworks Computational reproducibility in financial research through multi-analyst studies Machine learning applications in banking and credit scoring Ethical considerations in algorithmic finance Professor Pérignon's recent work demonstrates a significant shift toward examining the reproducibility crisis in financial research and the integration of machine learning techniques into traditional risk management frameworks. His influential paper "Nonstandard Errors" in the Journal of Finance (2024) examines how different research teams approach the same financial questions, highlighting methodological challenges in the field. He has also pioneered tools like "The Risk Map" for validating risk models, which has practical applications for financial institutions. His scholarly impact is substantial, with over 38 published papers that have collectively received more than 50,000 downloads and 499 citations on SSRN, placing him among the top researchers in finance. His collaborative work spans multiple international institutions, reflecting the global nature of financial research and regulation. As Associate Dean for Research at HEC Paris, Professor Pérignon plays a key leadership role in shaping the research agenda of one of Europe's premier business schools. His work continues to influence both academic discourse and practical risk management approaches in the financial industry worldwide.

Andrés Mora Valenciaمشاهده پروفایل

دانشیار

• Quantitative Finance
• Financial Risk Quantification
• Machine Learning in Finance
• +۱۲ مورد دیگر

Andrés Mora Valencia is an Associate Professor in the Faculty of Management at the University of the Andes, Colombia, where he teaches in the Finance department. He holds a Doctor of Economics from the University of Salamanca and has a strong interdisciplinary background in industrial engineering, statistics, and quantitative finance. Education: Doctor of Economics, University of Salamanca, Spain (2014) Master's Degree in Banking and Quantitative Finance, University of Castilla-La Mancha, Spain (2013) Master in Industrial Engineering, University of the Andes, Colombia (2000) Statistics Specialist, National University of Colombia (2011) Advanced Mathematics Specialist, National University of Colombia (2003) Industrial Engineer, University of the Valley, Colombia (1997) His research focuses on quantitative finance , particularly financial risk quantification using advanced statistical and machine learning models. Key areas include Value-at-Risk (VaR), Expected Shortfall (ES), fat-tailed distributions, and volatility forecasting in cryptocurrency and energy markets. He actively applies Bayesian deep learning, semi-nonparametric methods, and real options theory to financial modeling. His recent publications (2021–2025) reveal a strong trend in applying machine learning to forecast financial indices like the VIX and Skew Index, analyzing higher-order moment spillovers in crypto-forex integration, and assessing risk in renewable energy and carbon markets. His work bridges econometrics, computational finance, and sustainability. Scientific Contributions: Developed models for skew index forecasting using machine learning Explored adaptive market hypothesis in Latin American and emission trading markets Advanced semi-nonparametric risk assessment for cryptocurrencies Applied real options to value renewable energy projects He advises several graduate and doctoral students in finance, including Andrés Raúl Cruz-Hernández, Esteban Nicolás Vanegas Herrera, and Leonardo Gerardo Santana Viloria. He teaches courses such as Derivatives, Financial Planning, Investment Decisions, and Computational Methods in Finance across undergraduate, master’s, and doctoral programs. He is affiliated with the Finance and Financial Economics research group at Uniandes.

Jie Chengمشاهده پروفایل

مدرس

• Financial Risk Management
• Econometrics
• Mathematical Statistics
• +۷ مورد دیگر

Jie Cheng is a Lecturer in Mathematics at Keele University, affiliated with the School of Computer Science and Mathematics. Their research focuses on econometric modeling, financial risk management, and statistical applications in education and economics. Key areas include Hidden Markov Models, regime-switching dynamics, and portfolio risk analysis, with notable contributions to cryptocurrency volatility studies and teaching excellence metrics. Education background and academic history are not explicitly detailed in the provided text, but their work spans quantitative finance, mathematical statistics, and educational policy analysis. Research themes emphasize statistical methodologies applied to real-world problems in finance, education, and macroeconomic systems. Publications highlight a strong focus on risk modeling, including papers on density forecasts, cryptocurrency portfolio VaR, and latent-factor-driven financial models. Their work bridges theoretical statistical frameworks with practical applications in market volatility analysis and educational metrics design. Professional activities include contributions to journals like Computational Economics , Empirical Economics , and Educational Review . Current roles involve teaching and research coordination within the School of Computer Science and Mathematics.

Pedram Jahangiryمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Time Series Forecasting
• +۵ مورد دیگر

Pedram Jahangiry is a Professional Practice Assistant Professor in the Department of Data Analytics and Information Systems at the Huntsman School of Business, Utah State University. He holds a PhD in Economics from Arizona State University (2017), an MBA from Sharif University (2012), and a Master’s in Economics from Simon Fraser University (2013). His research focuses on machine learning applications in finance, deep learning, time series forecasting, and empirical asset pricing. He previously worked as a research associate at BlackRock NYC within the Financial Modeling Group. Teaching Responsibilities: Pedram teaches advanced courses such as Advanced Machine Learning for Analytics, Deep Forecasting, Econometrics, and Machine Learning in Economics and Finance. His teaching has been recognized with the 2024 USU Eldon J Gardner Teacher of the Year and Huntsman School Teacher of the Year awards. Research & Projects: As a project mentor at the Analytics Solutions Center (ASC), he facilitates experiential learning for students through corporate analytics collaborations. His research bridges econometrics and machine learning, addressing topics like household financial obligations’ impact on equity premiums and algorithmic trading strategies. Awards: USU Eldon J Gardner Teacher of the Year (2024) Huntsman School Teacher of the Year (2024) Professional Roles: Active in academic and industry partnerships, Pedram integrates practical finance challenges into his teaching and research agendas, emphasizing real-world data analytics solutions.

Somayyeh Lotfiمشاهده پروفایل

مدرس

• Behavioral Finance
• Risk Management
• Financial Optimization
• +۳ مورد دیگر

Dr. Somayyeh Lotfi is a Lecturer in Finance and Deputy Course Leader of the BA (Hons) in Accounting and Finance at the University of Central Lancashire Cyprus. She holds dual PhDs in Finance (University of Cyprus, 2022) and Applied Mathematics (University of Guilan, 2016), alongside advanced degrees in Applied Mathematics. Her research focuses on financial optimization, risk management under ambiguity, and behavioral finance, with notable contributions in political risk hedging, Eurozone debt analysis, and robust portfolio models. She is a member of the Financial Management Association and Multinational Finance Society. Education: PhD in Finance, University of Cyprus (2022) PhD in Applied Mathematics, University of Guilan (2016) MSc in Applied Mathematics, Ferdowsi University of Mashhad (2011) BSc in Applied Mathematics, Ferdowsi University of Mashhad (2009) Her research bridges theoretical models with practical financial decision-making, addressing topics like equity home bias and household under-diversification. She has received prestigious awards, including the Outstanding Peer-Reviewer Award (2018) and the Best Doctoral Paper Award (2017). Her work has been published in high-impact journals like the European Journal of Operational Research and Journal of Financial Stability. Awards/Grants: University of Cyprus Scholarship (2017-2022) Iran Ministry of Science and Technology grant for research abroad (2015) Dr. Lotfi actively contributes to academic discourse through seminars and conferences, including presentations at the World Finance Conference (2024) and the Multinational Finance Society Annual Conference (2023). She teaches modules in Corporate Finance, Investments, and International Financial Economics, emphasizing practical applications of financial theory.

Chang Pao Liمشاهده پروفایل

دانشیار

• International Economics
• International Trade
• Political Economy of Trade Policies
• +۷ مورد دیگر

Dr. Chang Pao Li is a full-time Associate Professor of Economics at the School of Economics, Singapore Management University (SMU) , where she also serves as Associate Dean (Research and Industry Engagement) and sits on the University Tribunal . Her academic journey began with a B.A. in Business Administration from National Taiwan University (1994), followed by an M.A. and Ph.D. in Economics from the University of Michigan (1997, 2002). Research Priorities: Dr. Chang's work bridges GATT/WTO institutional analysis Political economy of trade policy Empirical trade-foreign direct investment (FDI) linkages Quantitative trade flow modeling Education systems' impact on human capital and trade patterns Flood resilience in firm performance Global value chain dynamics Spatial dependence in production functions Notable Contributions: Her 2022 World Economy paper on global value chains and the CPTPP, and the 2022 Management Science study on country image effects in trade stand out among her 15 most recent publications. Current projects examine WTO concessions balancing Preferential trade agreement depth Flood impact on Chinese firms China-U.S. trade war responses Global value chain effects on Singapore Academic Leadership: As Associate Dean, she drives industry engagement strategies while advising PhD candidates like Wei Jin , Yuting Chen , and Phuong Nguyen . Though no specific awards are listed, her editorial roles at Review of World Economics and Economic Modelling underscore scholarly influence. Methodological Impact: Known for expanding Penrose's power index simulations and developing spatial econometric frameworks for production function estimation, her work appears in top journals including Journal of International Economics , Journal of Development Economics , and Mathematical Social Sciences .

Kostas Giannopoulosمشاهده پروفایل

استاد

• Market Risk Management
• Option Pricing
• Volatility Modelling
• +۴ مورد دیگر

Professor Kostas Giannopoulos holds the position of Professor of Finance at the School of Economics, Business and Computer Science, Neapolis University, Pafos. He is a Doctor Laureate in Banking and Economics from the University of Siena, with additional degrees from the University of Wales (Masters in Banking and Finance) and London Guildhall University (PhD in Finance). His research focuses on volatility modeling, market risk analysis, option pricing, and international portfolio diversification. Notable contributions include the Filtered Historical Simulation (FHS) model, which revolutionized risk forecasting in finance. Key achievements include a 1999 Journal of Futures Markets publication ranked as the most influential in commodities and 5th overall in the journal's 40-year history, and a 1997 European Journal of Finance article ranked 13th in its 25-year history. He has advised financial institutions and regulatory bodies for over 30 years, and served as regional chapter director for PRMIA. His work spans commodity markets, derivatives risk, and systemic financial risk in global contexts. Active in academic speaking engagements and professional seminars across Europe and the U.S., he maintains strong industry ties. Research emphasizes volatility spillovers, risk modeling under uncertainty, and applying FHS to CCP default funds and waterfall mechanisms. His work bridges theoretical finance with practical applications in regulatory frameworks and portfolio management strategies. Ongoing interests include cryptocurrency risk analysis and long-horizon volatility forecasting.

Lasse Borkمشاهده پروفایل

استاد

• Asset Pricing
• Financial Econometrics
• Empirical Finance
• +۵ مورد دیگر

Lasse Bork is a Professor of Finance at the Aalborg University Business School, part of Aalborg University's Faculty of Humanities and Social Sciences. His research focuses on asset pricing, financial econometrics, and empirical finance, with a particular emphasis on macro-finance linkages, housing markets, and energy markets. He has published in top journals such as Management Science , Journal of Banking and Finance , and Real Estate Economics . Bork also holds an external role as Senior Quantitative Researcher at Norlys Energy Trading A/S. Research Interests: Bork's work explores topics like commodity currency hypotheses, housing sentiment indices, and the effects of unconventional monetary policy. His methodologies include factor analysis, dynamic model averaging, and state-space models. Recent projects include analyzing climate risk reporting by pension funds and the impact of Federal Reserve asset purchases on economic outcomes. Awards: Recipient of 'Teacher of the Year 2014' (Aalborg University) Recipient of 'Teacher of the Year 2014' at the Faculty of Social Sciences Årets underviser 2014 (Aalborg University) Grants & Projects: Climate Risk Reporting by Pension Funds: International Evidence (2021–2023) Nordjysk Konjunkturbarometer 2 (2007–2019) Labs/Teams: Collaborations include work with institutions such as KU Leuven, Aarhus University, and participation in international conferences on financial econometrics and macroeconomic policy.

• Corporate Finance
• Financial Management
• Mergers & Acquisitions
• +۳ مورد دیگر

Matteo Rossi is an Associate Professor at the Department of Law, Economics, Management and Quantitative Methods (DEMM) of Università degli Studi del Sannio (Italy), specializing in Corporate Finance . He has published extensively on financial decision-making, capital structure, and strategic management in SMEs and high-tech industries. Research Focus: Corporate finance, M&A dynamics, capital budgeting practices, and export strategies in pharmaceutical/IT sectors Geographic Scope: Studies span Italy, India, UK, and global financial markets Methodological Expertise: Quantitative analysis, secondary data research, AHP modeling, and literature reviews Recent work examines ambidexterity in Asian venture capital , calendar anomalies in investment decisions , and post-COVID digital healthcare opportunities . While no formal awards or student advising details are listed in the provided data, his collaborative publications with researchers from multiple countries (France, India, China, etc.) demonstrate strong international engagement.

Francesco Ravazzoloمشاهده پروفایل

استاد

• Bayesian econometrics
• Energy economics
• Financial econometrics
• +۶ مورد دیگر

Francesco Ravazzolo is Full Professor of Econometrics at the Free University of Bozen-Bolzano (Faculty of Economics and Management) and Head of the Department of Data Science & Analytics at BI Norwegian Business School. He also acts as visiting professor at BI’s Center for Applied Macro and Commodity Prices and is president of the Society of Nonlinear Dynamics and Econometrics. Education: Ph.D. in Econometrics, Tinbergen Institute & Erasmus University Rotterdam, 2007 Research domains: Ravazzolo’s work sits at the intersection of Bayesian econometrics, energy economics, financial econometrics and macroeconometrics. He develops forecasting methods for electricity prices, commodity markets, cryptocurrencies and macro aggregates, with particular emphasis on regime-switching, mixed-frequency and high-dimensional techniques. His recent projects exploit textual data and semantic networks to predict consumer confidence and financial volatility. Publication trends: Since 2022 he has published extensively on (i) hourly/daily electricity-price forecasting with renewables and macro drivers, (ii) robust or Markov-switching time-series methodologies, (iii) big-data sentiment indicators for bond and stock markets, and (iv) energy-commodity and crypto-asset econometrics. Applications span European gas-price caps, Italian zonal imbalances, Nordic and German power markets, and global CDS indices. Editorial & professional service: Editorial boards: Annals of Applied Statistics, International Journal of Forecasting, Journal of Applied Econometrics, Spatial Economic Analysis, Studies in Nonlinear Dynamics & Econometrics President, Society of Nonlinear Dynamics & Econometrics Grants & enterprise: He is co-founder of three academic spin-offs: AIAQUA (AI for water and energy management), COMMODIA (commodity-data analytics) and STAIRS (forecasting platforms), securing continuous grant and industry funding for applied research. Labs & teams: At BI he leads the Department of Data Science & Analytics (ca. 40 faculty) and coordinates the Commodity Prices Research Hub; at unibz he supervises the Econometrics & Forecasting group involved in regional energy-market projects.

• International Macroeconomics
• Monetary Policy
• Financial Markets
• +۳ مورد دیگر

Sandra Eickmeier is a Research Professor at the Research Centre of the Deutsche Bundesbank. Her work focuses on international macroeconomics, monetary policy, and financial markets with an emphasis on applied macroeconometrics and expectation formation. She has contributed extensively to understanding global inflation dynamics, financial stress transmission channels, and the effectiveness of monetary policy in varying economic conditions. Her research explores topics such as banking regulation, credit supply shocks, and the interplay between macroeconomic factors and financial stability. Notable contributions include studies on China's role in global inflation, time-varying macro-financial linkages, and the interest rate pass-through mechanisms in the Eurozone during crises. Eickmeier has co-authored over 30 peer-reviewed articles and working papers, including key analyses on financial shocks and inflation dynamics. Her work often employs advanced econometric techniques like factor-augmented vector autoregression (FAVAR) and global VAR models to disentangle complex economic relationships.

Stefan Weberمشاهده پروفایل

استاد

• Financial Mathematics
• Actuarial Science
• Risk Management
• +۳ مورد دیگر

Prof. Stefan Weber is a Professor in Insurance and Financial Mathematics at Leibniz University Hannover. He holds a position in the Faculty of Economics and Management, Department of Insurance and Financial Mathematics. His research focuses on risk measurement, systemic risk, cyber insurance, and actuarial science. He has published extensively on topics such as distortion risk measures, Monte Carlo methods in finance, and resilience in complex systems. Prof. Weber's work bridges theoretical advancements and practical applications, addressing challenges in financial regulation, insurance pricing, and cybersecurity. His recent research emphasizes the integration of machine learning and AI into risk assessment frameworks, particularly in cyber risk modeling. He has contributed to debates on solvency regulation (e.g., Solvency II) and systemic risk mitigation in financial networks. His academic contributions reflect a strong interdisciplinary approach, leveraging stochastic modeling, computational finance, and systemic analysis to inform both academic and industry practices. He is actively involved in the academic community through publications and collaborations in financial mathematics and actuarial science.

Michael Fuمشاهده پروفایل

استاد

• Simulation Modeling
• Operations Research
• Financial Engineering
• +۵ مورد دیگر

Michael Fu is the Smith Chair of Management Science at the Robert H. Smith School of Business, University of Maryland, with joint appointments in the Institute for Systems Research and the Department of Electrical and Computer Engineering. He holds a Professor rank and specializes in simulation optimization, stochastic processes, and financial engineering. His research bridges theoretical advancements and practical applications in supply chain management, healthcare systems, and artificial intelligence. His academic accolades include INFORMS College on Simulation's Outstanding Publication Award (1998, 2019), IEEE Fellow (2007), and INFORMS Simulation Society's Distinguished Service Award (2018). He has held editorial roles at journals such as Management Science and Operations Research , and served as Program Director at the National Science Foundation (2010–2012, 2015). Research Focus: Simulation optimization, stochastic control, and Monte Carlo methods applied to healthcare, finance, and manufacturing. Key Contributions: Co-author of seminal works like Conditional Monte Carlo and Simulation-Based Algorithms for Markov Decision Processes . Recent Trends: Integrating AI techniques (e.g., Monte Carlo Tree Search) with classical stochastic optimization frameworks. Fu's interdisciplinary work spans multiple domains, including kidney transplantation logistics, financial risk modeling, and pandemic response strategies (e.g., real-time COVID-19 decision support systems). He has advised numerous research initiatives funded by NSF, SEMATECH, and Air Force Office of Scientific Research, emphasizing practical applications of stochastic methods.