جست‌وجوی استاد و پژوهشگر

Greg Tkaczمشاهده پروفایل

استاد

• Macroeconomics
• Monetary Policy
• Econometric Forecasting
• +۳ مورد دیگر

Greg Tkacz is a Professor of Economics at Saint Francis Xavier University (StFX), serving since 2010. Previously, he spent 14 years at the Bank of Canada as a researcher and manager, specializing in monetary policy analysis and macroeconomic forecasting. His current research focuses on high-frequency electronic payments data (e.g., debit/credit card transactions) to measure economic activity in real-time, funded by SSHRC grants. Tkacz has published extensively in journals like the Journal of Econometrics and served on editorial boards including the Canadian Journal of Economics and Canadian Public Policy . He earned his Ph.D. in Economics from McGill University. His work often addresses policy-relevant questions, such as the impact of the 2008 financial crisis and the economic effects of extreme events like 9/11. Tkacz’s research has been featured in global media including the Wall Street Journal , Le Figaro , and Canadian outlets like CBC. As Economics Department Chair (2013–2021), he oversaw significant growth in student enrollment and hosted the 2017 Canadian Economics Association conference, attracting 800+ delegates. Tkacz’s teaching emphasizes applying economic theory to real-world problems, mentoring over 20 Honours students who have won national scholarships, Rhodes Scholarships, and top Canadian undergraduate research awards. His students have entered top graduate programs and secured roles at institutions like the Bank of Canada.

• Financial Econometrics
• Asset Pricing
• Market Microstructure
• +۳ مورد دیگر

Jia Liu is an Associate Professor of Finance at the Sobey School of Business, Saint Mary's University. His research focuses on financial econometrics, asset pricing, and quantitative finance, with an emphasis on Bayesian methods and market dynamics. He has contributed to the analysis of market regimes, volatility modeling, and high-frequency financial data. His work often addresses topics such as bull/bear market identification, realized covariance estimation, and pairs trading strategies. Key methodologies include Bayesian nonparametric techniques and stochastic volatility models. He has published in top-tier journals, with a focus on empirical financial analysis and econometric modeling. No scientific awards or grants are explicitly mentioned in the provided text. No advisees or students are listed, and there is no information about affiliated labs or teams.

• Financial Mathematics
• Applied Probability
• Derivatives Pricing
• +۴ مورد دیگر

Matthew Lorig is a Professor in the Department of Applied Mathematics at the University of Washington, where he teaches in the Masters and PhD programs in Applied Mathematics, as well as the Computational Finance and Risk Management program. Prior to this role, he served as a Postdoctoral Researcher and Lecturer at Princeton University's Department of Operations Research and Financial Engineering. His academic journey includes a PhD in Physics from the University of California, Santa Barbara, and a BS in Physics from the University of Minnesota. Dr. Lorig's research focuses on Financial Mathematics and Applied Probability, with particular emphasis on derivative pricing, implied volatility dynamics, optimal investment strategies, static hedging, and algorithmic trading. Recently, he has expanded his work to include optimal bookmaking strategies for betting markets. His contributions have been recognized through awards such as the SIAM Activity Group on Financial Mathematics and Engineering (SIAG/FME) Early Career Prize in 2016. He has held visiting appointments, including a Visiting Professorship at the University of Chile's Department of Engineering Mathematics in 2019. Currently, he serves as an Associate Editor for Applied Mathematical Finance and the SIAM Journal on Financial Mathematics . His research outputs include over 40 peer-reviewed articles, with a focus on mathematical finance, stochastic processes, and their applications to real-world financial systems. Education: PhD in Physics, University of California, Santa Barbara (Year not specified) BS in Physics, University of Minnesota (Year not specified) Awards: SIAG/FME Early Career Prize (2016) Professional Roles: Associate Editor, Applied Mathematical Finance Associate Editor, SIAM Journal on Financial Mathematics

• Macroeconomics
• Monetary Policy
• Climate Change and Agriculture
• +۳ مورد دیگر

Param Silvapulle is a Professor in the Department of Econometrics and Business Statistics at Monash University, specializing in econometric and statistical methodologies. Her research focuses on Macroeconomics, Monetary Policy, Climate Change impacts on agriculture, and panel data modelling. She leads projects like a nonparametric time-varying coefficient model for climate change and agriculture, and a big data approach to assess climate impacts on wheat crops in Victoria. Her educational background includes a Ph.D. in statistics, though specific details are not explicitly stated in the provided texts. She has collaborated on projects funded by the ARC, including work on operational risk modelling and copula-based financial time series analysis. Key research areas include estimating financial risks (credit, market, operational), panel data analysis, and climate change's economic implications. Her recent work emphasizes climate change's agricultural effects and financial market volatility. She has published extensively in journals like the Journal of Business and Economic Statistics and Energy Economics , focusing on econometric models and their applications. Notable projects include analyzing oil-stock price relationships in net oil importing countries and developing methods for operational risk assessment. Her work contributes to UN SDGs related to sustainable development and economic stability.

Jukka Lassilaمشاهده پروفایل

استاد

• Energy Engineering
• Power Systems
• Smart Grids
• +۴ مورد دیگر

Jukka Lassila is a tenured Full Professor at the Department of Electrical Engineering within the LUT School of Energy Systems at Lappeenranta-Lahti University of Technology LUT. His research focuses on energy transition challenges, particularly in electricity distribution networks, renewable energy integration, and smart grid technologies. He leads studies on grid asset management, demand response mechanisms, and the socio-technical impacts of distributed energy resources. Key areas of investigation include: Optimization of distribution network tariffs and customer behavior under volatile energy markets Climate resilience of infrastructure (e.g., wind/snow impact on grid reliability) Hydrogen valley feasibility studies in Finnish regions Electric vehicle charging dynamics in Nordic climates Multi-functional energy storage systems for grid stability Recent work highlights trends toward: Integration of distributed generation and storage Market-driven grid flexibility solutions Climate adaptation of rural distribution networks His research outputs emphasize practical applications for distribution system operators (DSOs), including tariff design frameworks, outage mitigation strategies, and techno-economic analysis of emerging technologies like vehicle-to-grid interfaces.

• Asset Pricing
• Commodity Markets
• Financial Derivatives
• +۳ مورد دیگر

Professor Chardin Wese Simen is a Professor of Finance at the University of Liverpool Management School. He specializes in asset pricing, commodity markets, derivatives, and financial econometrics. He holds a BSc (1st class) from the University of Wales, an MSc in International Securities, Investment and Banking, and a PhD in Finance from the University of Reading. Chardin has published extensively in top journals such as the Journal of Econometrics and Management Science. His research has been recognized with awards from the British Academy and multiple teaching excellence certificates. He serves as Associate Editor for the Journal of Commodity Markets and The European Journal of Finance, and regularly reviews for leading journals. As Director of Studies for the BSc Finance and Data Analytics program, he oversees curriculum development and strategic direction. He has designed and delivered courses across BSc, MSc, PhD, and executive education programs. He is an external examiner for Cranfield School of Management’s MBA and Executive MBA programs and the University of East Anglia’s postgraduate finance programs. His professional activities include roles at conferences like the American Finance Association, editorial memberships, and PhD examination across institutions such as ESSEC Business School. He actively participates in the Sustainable Finance and Governance Workshop and contributes to research funding initiatives. Key research themes include commodity market dynamics, variance risk, and econometric modeling. His recent work explores global equity premium predictability and convenience yield risk in commodity markets. He has advised numerous PhD students now in academia and industry.

Daniele Massacciمشاهده پروفایل

دانشیار

• Econometrics
• Financial Economics
• Empirical Asset Pricing
• +۴ مورد دیگر

Daniele Massacci is an Associate Professor (Reader) in Data Analytics for Finance at King’s Business School, King’s College London since August 2019. He holds an MSc from the University of Warwick and a PhD from the University of Cambridge. His research focuses on Econometrics and Finance, with expertise in Empirical Asset Pricing, Financial Econometrics, and High Dimensional Statistics. Prior roles include academic positions at the University of Warwick, University of Surrey, Einaudi Institute for the Economics and Finance (EIEF), and as Senior Research Economist at the Bank of England. He is affiliated with the Qatar Centre for Global Banking & Finance, advancing central bank policy research. His work emphasizes quantitative methods to analyze financial markets and macroeconomic trends. Education: MSc, University of Warwick PhD, University of Cambridge Research Interests: Massacci’s work bridges econometric theory and financial applications, with emphasis on: Factor models in asset pricing Structural change analysis in financial markets Risk management under uncertainty Large-scale portfolio dynamics His recent studies explore regime changes, pandemic market impacts, and forecasting under structural breaks. Grants & Advising: Active in central bank policy research, collaborating with institutions like the Qatar Centre. No formal advisees listed but contributes to academic programs like the Financial Policy & Regulation MSc. Lab/Affiliations: Qatar Centre for Global Banking & Finance, dedicated to central bank policy analysis across monetary policy and financial regulation.

Christian Dorionمشاهده پروفایل

دانشیار

• Derivatives Pricing
• Volatility Modeling
• Risk Management
• +۳ مورد دیگر

Christian Dorion is an Associate Professor in the Department of Finance at HEC Montréal. He holds a Ph.D. in Finance from McGill University, an M.Sc. in Computer Science from Université de Montréal, and a B.Sc. in Mathematics and Computer Science from Université de Montréal. His expertise spans derivatives pricing, volatility modeling, credit risk analysis, and financial econometrics. He is actively involved in academic research and has published in top-tier journals such as The Review of Financial Studies. Education : Ph.D. (Finance), McGill University M.Sc. (Computer Science), Université de Montréal B.Sc. (Mathematics and Computer Science), Université de Montréal Research Interests : Dorion focuses on financial risk management, volatility dynamics, and the application of econometric methods to financial markets. His work bridges theory and practice, addressing issues like inflation’s impact on equity valuations, jump risk in equity returns, and systematic risk in credit derivatives. Advising : He has advised 24 students across doctoral and master’s programs, including topics like multivariate stochastic volatility models, real estate diversification benefits, and machine learning in finance. Recent student projects include volatility forecasting with GARCH-enhanced models and crisis simulation in mortgage portfolios. Labs/Teams : While no specific lab is mentioned, his research aligns with HEC Montréal’s finance and risk management initiatives.

David Benatiaمشاهده پروفایل

دانشیار

• Applied Econometrics
• Theoretical Econometrics
• Energy Economics
• +۵ مورد دیگر

David Benatia is an Associate Professor in the Department of Applied Economics at HEC Montréal (Université de Montréal), Canada, since 2020. He holds part-time affiliations with CREST (ENSAE, Institut Polytechnique de Paris) as a researcher and lecturer, and collaborates with Laboratoire de Finance des Marchés de l’Énergie (FIME) in France. His expertise spans Applied and Theoretical Econometrics, Energy Economics, Environmental Economics, and Industrial Organization. Education: Ph.D. in Economics from Université de Montréal, followed by advanced studies in Environmental Economics, Economics, and Mathematics at Toulouse School of Economics. Research interests focus on econometric methodologies, energy market dynamics, climate change policies, and pandemic impacts on economic systems. Recent work explores systemic risk in banking, air pollution health effects, and electricity market vulnerabilities during the pandemic. He has supervised 7 Master’s theses and projects analyzing ESG criteria, energy transitions, and financial sector connectedness. Teaching includes courses on environmental economic challenges and applied econometrics integrating machine learning techniques. His research is published in top journals such as Journal of Econometrics and Energy Journal .

• Financial Asset Valuation
• Portfolio Management
• Risk Measurement and Management
• +۵ مورد دیگر

Yannick Malevergne is a Professor of Finance at the University of Paris 1 Panthéon-Sorbonne, affiliated with the Sorbonne School of Management and the PRISM research center. He serves as Deputy Director of the Sorbonne School of Management and Director of the Finance division at PRISM. His research focuses on financial asset valuation, risk management, and statistical properties of financial markets, with contributions to econophysics and multifractal analysis. Education: Doctorate in Physics (2000-2002), University of Nice Sophia-Antipolis DEA and Agrégation in Physics (1996-2000), École Normale Supérieure de Lyon Higher Education Aggregation in Management Sciences (2007) Accreditation to Supervise Research (2006), University of Lyon III Research Interests: Malevergne's work bridges finance and physics, exploring topics like financial risk modeling, asset pricing, market dynamics, and the statistical properties of financial time series. His studies address bubbles, crashes, portfolio optimization, and the application of multifractal analysis to financial data. Recent Article Trends: His recent work includes modeling financial bubbles and drawdowns, microstructure analysis using Cox-BESQ processes, and risk apportionment theories. He also investigates macroeconomic leverage dynamics and volatility correlations in asset returns. Professional Roles: Malevergne has held academic positions at Jean Monnet University (2007-2016) and part-time roles at EMLYON Business School (2004-2016). He has collaborated with institutions like ETH Zurich and contributed to financial risk research through publications and books like Extreme Financial Risks: From Dependence to Risk Management . Labs/Teams: His affiliations include the PRISM research center and Sorbonne School of Management, where he leads the Finance division. His work integrates interdisciplinary approaches from economics, physics, and signal processing.

• Corporate Finance
• Asset Pricing
• Household Finance
• +۴ مورد دیگر

Raffaele Corvino is an Assistant Professor in the Department of Finance at NEOMA Business School. His research focuses on corporate finance, asset pricing, household finance, and credit risk, emphasizing structural estimation methods. He holds a PhD in Finance and has taught at institutions including Collegio Carlo Alberto and Bayes Business School. During his doctoral studies, he visited the National University of Singapore. His work has been recognized with the Best Doctoral Thesis Award at Bayes Business School. Research interests include analyzing financial stability, sovereign credit risk post-Eurozone crisis, and pandemic tail risks. He has published in top journals such as the Journal of Corporate Finance and Journal of Financial Stability. His grants include funding from INQUIRE Europe and the Observatoire de l'Espargne Europeenne (OEE). He frequently presents at major conferences like the American Finance Association and European Finance Association meetings. Raffaele’s articles explore topics ranging from market leverage dynamics to hedging strategies for income volatility. His awards highlight contributions to understanding corporate governance and sovereign debt pricing. Collaborations with institutions like the Risk Management Institute at NUS reflect his interdisciplinary approach to financial challenges.

• Machine Learning
• Chemical Engineering
• Computational Chemistry
• +۱۱ مورد دیگر

Charles McGill, Ph.D., is a researcher affiliated with the College of Engineering and the Department of Chemical & Life Science Engineering at Virginia Commonwealth University. His work bridges machine learning and chemical engineering, focusing on predictive modeling of molecular properties and reaction systems. Key research areas: Vapor-Liquid Equilibrium , Homogeneous Catalysis , Uncertainty Quantification , and Molecular Discovery . Lead developer of the Chemprop software suite for chemical property prediction using AI/ML. Recent publications highlight applications of deep learning and neural networks in thermodynamics and reaction mechanism analysis. His work integrates computational tools with experimental validation, advancing automated chemical systems. No awards or grants mentioned in available data. Collaborates on projects involving spectroscopy, kinetic modeling, and catalytic design.

• Machine Learning
• Artificial Intelligence
• Blockchain
• +۱ مورد دیگر

Dr. Ismail Alarab serves as a Lecturer in Computer Science at Bournemouth University, teaching core subjects including Artificial Intelligence, Databases, Big Data, and Programming. His research focuses on applied artificial intelligence with specialized expertise in blockchain technology and real-world machine learning applications, culminating in a December 2022 PhD from Bournemouth University. PhD in Novel Machine Learning Models Based on Uncertainty Estimation and Sequential Predictions for Blockchain Networks, Bournemouth University, 2022 MSc in Signal and Image Processing: Applications and Methods, Université Grenoble Alpes, France Diploma in Telecommunication Engineering (equivalent to MEng), Lebanese University, Lebanon Alarab's research centers on machine learning for blockchain systems, particularly uncertainty estimation in neural networks and graph-based models for cryptocurrency transaction analysis. His work bridges theoretical AI frameworks with practical financial security applications, emphasizing adversarial robustness and sequential prediction capabilities in distributed ledgers. Key methodologies include graph convolutional networks and LSTM architectures adapted for anti-money laundering systems. Analysis of his 14 publications (2020-2024) reveals consistent focus on machine learning applications in blockchain environments, with evolving sophistication from foundational transaction analysis (2020) to advanced uncertainty-aware sequential prediction models (2024). Dominant themes include adversarial attack methodologies for graph neural networks, thermal comfort optimization extensions, and specialized techniques for imbalanced financial datasets. No major scientific awards are documented, though professional development includes Leadership and Management Training (AshorneHill, 2023), Deploying Machine Learning Models in Production certification (DeepLearning.AI, 2023), and Microsoft Azure Fundamentals (AZ-900, 2023). Current advising activities and research grants are not specified in available materials. Dr. Alarab serves as reviewer for IEEE Transactions on Knowledge and Data Engineering and Elsevier's Neural Networks journal, indicating active peer engagement within academic communities. His research operates within Bournemouth University's Department of Computer Science, primarily collaborating with Dr. Prakoonwit and Dr. Nacer on blockchain-AI integration projects. While no dedicated lab is mentioned, his work demonstrates strong cross-institutional partnerships in financial technology security research.

• Financial Econometrics
• Market Microstructure
• Nonlinear Filtering
• +۷ مورد دیگر

Giuseppe Buccheri serves as an Associate Professor of Econometrics (13/ECON-05) in the Department of Economics at the University of Verona since 2024. Previously, he held positions as senior assistant professor (RTDB) at the same department and junior assistant professor (RTDA) at the Department of Economics and Finance of University of Rome Tor Vergata. His academic appointments include membership on the Faculty Board of PhD in Economics and Finance, Collegio didattico di Economics and Data Analysis, Research Commission, and Economics Department Council. His educational background features a PhD in Financial Mathematics from Scuola Normale Superiore, an MSc in Finance from Bocconi University, and an MSc in Theoretical Physics from the University of Catania. This interdisciplinary foundation supports his research at the intersection of financial theory, statistical methods, and economic applications. Dr. Buccheri's research focuses on advanced econometric techniques for financial markets, particularly in Financial Econometrics, Market Microstructure, Nonlinear Filtering, and Financial Economics. His work addresses critical challenges in time-series analysis, model validation, volatility estimation, and portfolio optimization. The JEL classifications of his research span C32 (Time-Series Models), C52 (Model Evaluation), C58 (Financial Econometrics), and G11 (Portfolio Choice). His recent publications demonstrate a strong trend toward developing sophisticated statistical methods for high-frequency financial data analysis, with particular emphasis on score-driven models, realized volatility/covariance estimation, and handling market microstructure effects. These contributions advance the field by addressing measurement errors, asynchronous trading, illiquidity effects, and model identification challenges in modern financial markets. As an educator, Dr. Buccheri teaches Financial Econometrics, Quantitative Methods for Business and Economics, and Time Series and Forecasting across multiple graduate programs including Master's degrees in Banking and Finance and Economics and Data Analysis, as well as PhD courses in Financial Time Series. He serves on various academic committees related to research, teaching, and PhD program governance. His research activities connect with several institutional resources including CIDE (Centro Interuniversitario di Econometria) and the broader econometric research community at the University of Verona. His work contributes significantly to the department's research profile in quantitative methods for economics and finance.

Robert Jungمشاهده پروفایل

استاد

• Financial Econometrics
• Discrete-Valued Time Series
• Computational Statistics
• +۲ مورد دیگر

Robert Jung is a Professor at the University of Hohenheim , affiliated with the Department of Econometrics and Economic Statistics under the Faculty of Business, Economics and Social Sciences . He joined the university in 2012 after holding professorships at the University of Erfurt and completing his postdoctoral degree at the University of Tuebingen. His current role includes serving as Vice-Dean for Research since 2014. Universities: University of Hohenheim, University of Erfurt, University of Tuebingen Departments: Econometrics and Economic Statistics, Computational Science Lab Research Interests span financial econometrics, discrete-valued time series, computational statistics, health econometrics, and spatial econometrics. His work focuses on modeling integer time series, volatility spillovers, and market interdependencies. Publication Trends include contributions to financial market analysis (2017-2006), discrete time series modeling (2015-2003), and cross-market dynamics (2014-1996). Key topics involve futures speculation, count data diagnostics, and stochastic volatility frameworks. Leadership Roles : Head of Department of Econometrics and Economic Statistics Speaker of Computational Science Lab Member of Faculty Board of Economics and Social Sciences