جست‌وجوی استاد و پژوهشگر

Johanna Ziegelمشاهده پروفایل

استاد

• Statistical Forecasting
• Probability Theory
• Financial Risk Management
• +۳ مورد دیگر

Johanna Ziegel is a Professor of Statistics at ETH Zurich, Switzerland, since 2024, and a Visiting Scientist at the Heidelberg Institute for Theoretical Studies (HITS). Previously, she held positions at the University of Bern, where she was promoted to Full Professor in 2023. Her research focuses on decision-theoretically sound methods for forecast evaluation, probabilistic forecasting, risk measures in finance, and applications in meteorology, medicine, and climate science. She is actively involved in editorial roles for journals like Bernoulli , JASA: Theory & Methods , and SIAM Journal on Financial Mathematics . Education: PhD in Stereological Analysis of Spatial Structures from ETH Zurich (2010), supervised by Paul Embrechts and Eva B. Vedel Jensen. Postdoctoral research at the University of Melbourne and Heidelberg University. Research Interests: Forecast evaluation, elicitable functionals, risk measures, isotonic regression, statistical calibration, and applications in finance, climate science, and biostatistics. Her work bridges theoretical statistics with practical challenges in uncertainty quantification and decision-making under uncertainty. Advising & Collaborations: Supervised 7 PhD students and mentored several postdocs. Collaborates with the Computational Statistics group at HITS and the Oeschger Centre for Climate Change Research. Her group explores distributional regression under order constraints and novel methods for forecast comparison. Recognition: Credit Suisse Award for Best Teaching (2022), H.I.T. Program for Academic Leadership (2021–2022). Active in professional service, including the Bernoulli Society Council and editorial boards.

Emese Lazarمشاهده پروفایل

استاد

• Risk Measurement
• Model Risk
• Financial Econometrics
• +۴ مورد دیگر

Professor Emese Lazar serves as Professor of Finance and Deputy School Director of Teaching and Learning at the ICMA Centre, Henley Business School, University of Reading. She joined the institution in 2005 and is an active member of the Econometrics with Data Science research cluster, focusing on quantitative finance applications. Her educational background includes: PhD in Finance from the University of Reading BSc in Finance and Banking from the University of Economic Studies, Bucharest BSc in Computer Science from the University of Bucharest Research interests span risk measurement and management , model risk , financial econometrics , derivatives pricing , green finance , climate risk in finance , and machine learning applications . Her work bridges theoretical finance with practical risk management challenges, particularly in climate-related financial risks and algorithmic risk modeling. Recent publications reveal a pronounced shift toward integrating climate risk metrics with traditional financial models and developing neural network-based forecasting for tail risk measures. Her research demonstrates consistent innovation in volatility modeling, model risk quantification, and climate finance applications across top-tier journals. Professor Lazar teaches postgraduate modules in Market Risk and Climate Change and Risk Management, supervising PhD students in her specialized research areas. She actively contributes to the Econometrics with Data Science research cluster, fostering interdisciplinary collaboration between finance, data science, and climate risk modeling.

Robert E. Hallمشاهده پروفایل

استاد

• Macroeconomics
• Monetary Policy
• Labor Economics
• +۶ مورد دیگر

Robert E. Hall is the Robert and Carole McNeil Senior Fellow at the Hoover Institution and Professor of Economics at Stanford University. A member of the National Academy of Sciences and fellow of several distinguished societies, he is an applied economist whose work focuses on macroeconomics, monetary policy, labor markets, taxation, and the economics of technology. Education: B.A., University of California, Berkeley Ph.D., Massachusetts Institute of Technology Research Interests: Hall’s research spans a broad range of topics including the economics of high technology and the Internet, stock market valuation, unemployment dynamics, business cycles, and fiscal and monetary policy. He has made foundational contributions to the understanding of consumption behavior, labor market fluctuations, and the design of efficient tax systems. His work on the flat tax with Alvin Rabushka significantly influenced public policy debates. Research Leadership and Policy Impact: Hall directs the NBER Program on Economic Fluctuations and Growth and chairs the NBER Business Cycle Dating Committee. He has advised the U.S. Treasury, Department of Justice, and Federal Reserve, and has testified before Congress on numerous occasions. His 2001 Ely Lecture to the American Economic Association highlighted his ongoing influence on macroeconomic thought. Scientific Awards and Honors: Member, National Academy of Sciences Fellow, American Academy of Arts and Sciences Fellow, Econometric Society Fellow, Society of Labor Economists Money Hall of Fame, Money Magazine (with Alvin Rabushka) Institutional Roles: In addition to his faculty role at Stanford, Hall serves as director of the NBER's Program on Economic Fluctuations and Growth and chairs its Business Cycle Dating Committee, which maintains the semi-official chronology of U.S. recessions.

Pablo Koch-Medinaمشاهده پروفایل

دانشیار

• Mathematical Finance
• Financial Economics
• Risk Management
• +۱ مورد دیگر

Pablo Koch-Medina is an Associate Professor of Finance and Insurance and Head of the Department of Finance at the University of Zurich (UZH). He is a faculty member of the Swiss Finance Institute and Director of the Center for Finance and Insurance, which bridges finance and insurance research. Before joining academia, he spent over 20 years in the finance and insurance industry. Research Interests: His work focuses on mathematical finance, financial economics, risk management, and insurance. Key themes include asset-liability management, valuation of insurance liabilities, and regulatory frameworks in financial markets. He emphasizes interdisciplinary approaches combining theoretical finance with practical insurance applications. Recent Articles: Recent publications explore capital adequacy, risk measurement frameworks, and the interplay between insurance and financial markets. His work addresses challenges like currency risk in capital requirements and the dynamics of long-term insurance products. Affiliations: Besides UZH, he contributes to the Swiss Finance Institute and the Center for Finance and Insurance. His research collaborates with institutions like the École Polytechnique Fédérale de Lausanne and Maastricht University. Future Work: Ongoing projects include optimizing insurance firm investment strategies and analyzing systemic risks in pension systems. He advocates for integrating digitalization and data science into traditional actuarial models.

Daumantas Bloznelisمشاهده پروفایل

دانشیار

• Applied Financial Econometrics
• Financial Markets Analysis
• Commodity Pricing
• +۵ مورد دیگر

Daumantas Bloznelis is an Associate Professor of Business Analytics at the Norwegian University of Life Sciences (Ås, Norway) and an Adjunct Associate Professor at the University of Inland Norway (Rena, Norway). He holds a PhD in Economics from the Norwegian University of Life Sciences, with visiting scholar experience at Cornell University (USA). His research focuses on financial econometrics, commodity markets, and statistical price modeling, with particular emphasis on risk management and forecasting in aquaculture sectors. Bloznelis has extensive experience in academia, including teaching courses on machine learning, econometrics, and quantitative methods across multiple universities. He has supervised numerous PhD and Master’s theses, contributing to the development of future scholars in finance and management. His work also extends to applied research, such as cross-hedging carbon risk and portfolio optimization in electric vehicle sectors. Bloznelis has received several accolades, including scholarships from the Norwegian Research Council and Vilnius University, and awards for academic excellence in Lithuania. Education: PhD in Economics/Finance (2011–2016), Norwegian University of Life Sciences MSc in Statistics/Econometrics (2009–2011), Vilnius University BSc in Statistics/Econometrics (2005–2009), Vilnius University Research Interests: Bloznelis specializes in statistical price modeling, forecasting methodologies, and risk management in financial and commodity markets. His work integrates machine learning and econometric techniques to address practical challenges in sectors like salmon farming and electric vehicles. He also explores the application of copula models and factor analysis to portfolio optimization and market dynamics. Key Awards: 3rd prize in International Econometric Team Competition (2010) PRESIDENT OF LITHUANIA AWARD for dictation contest (2007) Prime Minister of Lithuania Award for matriculation excellence (2005) Professional Contributions: Bloznelis has presented at over 30 international conferences, including NCCC commodity price analysis meetings and CEMA annual conferences. He serves on the Board of Advisors for Vilnius University’s Faculty of Mathematics and Informatics. His research outputs include influential papers on futures market biases, hedging strategies, and factor models in commodity pricing.

Andrew J. Pattonمشاهده پروفایل

استاد

• Financial Econometrics
• Volatility Forecasting
• High-Frequency Financial Data Analysis
• +۴ مورد دیگر

Andrew J. Patton is the Zelter Family Distinguished Professor and Professor of Economics at Duke University's Trinity College of Arts & Sciences, holding these roles since 2016 and 2013 respectively. Previously, he served as Associate Professor of Economics at Duke from 2009 to 2013. Patton holds a Ph.D. and M.A. from the University of California, San Diego (2002 and 2000), and a B.Bus. from the University of Technology Sydney (1998). His research focuses on financial econometrics, particularly volatility forecasting, dependence modeling, high-frequency financial data analysis, and hedge fund dynamics. He has published extensively in top journals such as the Journal of Finance , Journal of Financial Economics , and Econometrica . His recent work includes advancements in realized semicovariances, k-means clustering for unobserved heterogeneity, and risk price variations. Patton’s 2016 Distinguished Professorship reflects his scholarly impact. His research emphasizes practical applications, such as improving volatility forecasts and addressing market anomalies’ trading costs.

• Systemic Risk
• Financial Risk Management
• Credit Scoring
• +۴ مورد دیگر

Christophe Hurlin is a Professor at the University of Orleans specializing in finance, risk management, and econometrics. His research spans systemic risk measurement, credit scoring models, machine learning applications in finance, and computational reproducibility in financial research. With over 30 scholarly publications, he has established himself as a significant contributor to financial econometrics and risk management literature. His primary research interests focus on systemic risk measurement methodologies, credit scoring models incorporating machine learning techniques, and the reproducibility of financial research findings. Hurlin's work often bridges theoretical econometric frameworks with practical financial applications, particularly in banking regulation and risk modeling. He has developed innovative tools such as the Risk Map for validating risk models and has contributed significantly to understanding systemic risk through comprehensive surveys and comparative analyses of risk measures. Hurlin's publication record shows a clear evolution from traditional risk measurement techniques toward integrating machine learning approaches in finance, with recent work focusing on the fairness of credit scoring models, Bayesian approaches to default probability calibration, and the intersection of machine learning with regulatory capital requirements. His research demonstrates consistent engagement with both theoretical underpinnings and practical applications in financial risk management. Hurlin has collaborated extensively with researchers across Europe, particularly with colleagues from HEC Paris (notably Christophe Pérignon), creating a substantial body of work on systemic risk, credit scoring, and computational reproducibility. His most cited works include 'Where the Risks Lie: A Survey on Systemic Risk' and 'Nonstandard Errors,' reflecting his influence in both risk management and methodological research. His research has practical implications for banking regulation, financial stability monitoring, and the implementation of machine learning in credit assessment systems. Through his work on computational reproducibility, Hurlin has also contributed to improving research standards in financial economics, advocating for greater transparency and verification in published findings.

• Financial Econometrics
• Volatility Modeling
• Time Series Analysis
• +۴ مورد دیگر

Andrew J. Patton is a Professor in the Department of Economics at Duke University's Trinity College of Arts & Sciences, with a secondary affiliation at Singapore Management University's School of Economics. His office is located at 213 Social Sciences Building, Box 90097, Durham, NC 27708-0204, United States, and his professional website is available at http://econ.duke.edu/~ap172/. Dr. Patton's research focuses on financial econometrics, with particular expertise in volatility modeling, high-frequency data analysis, and dependence structures in financial markets. His work spans several key areas including realized variance methods, copula modeling for financial time series, risk management techniques, and forecast evaluation methodologies. He has made significant contributions to understanding market microstructure, hedge fund risk exposures, and the dynamics of financial correlations. His publication record demonstrates consistent output in top finance and econometrics journals, with recent work exploring machine learning applications in volatility forecasting, granular risk measures, and the impact of high-frequency data on asset pricing models. The research shows a clear trajectory toward increasingly sophisticated modeling of financial dependencies and risk structures. Dr. Patton has received recognition for his scholarly contributions, ranking among SSRN's top 1,427 authors by total paper downloads and top 1,090 by total paper citations. His work is frequently cited in the field of financial econometrics, indicating substantial influence on contemporary research. He maintains active research collaborations with leading economists including Tim Bollerslev, Kevin Sheppard, Tarun Ramadorai, and Robert Engle, among others. These collaborations span multiple institutions and have produced influential work on volatility modeling, risk measurement, and financial market dynamics.

Ralf Kellnerمشاهده پروفایل

استاد

• Economics
• Data Science
• Statistics
• +۲ مورد دیگر

Professor Ralf Kellner holds the Chair of Financial Data Analytics at the University of Passau, Faculty of Economics. His work integrates economics, data science, and statistics, focusing on empirical and application-oriented research to explore how statistical learning and AI can uncover insights in data-driven decision-making processes that generate economic value. He also teaches courses such as Deep Learning and Text Analysis in Finance, Financial Data Analytics and Machine Learning, and Scientific Computing with Python. His research examines the intersection of financial markets, statistical learning, and artificial intelligence, with specific interests in modeling adverse financial developments, systemic risks, and analyzing text data via domain-specific language models. Publications highlight collaborations with researchers like D. Rösch and N. Gatzert. Recent publications include work on hybrid service agents, quantile neural networks, default resolution time analysis, Bayesian sovereign bond risk models, and international diversification studies. His methodological approaches span extreme value theory, quantile regression, and multivariate statistical techniques applied to financial and insurance contexts. Contact: ralf.kellner@uni-passau.de

Edward Furmanمشاهده پروفایل

استاد

• Actuarial Science
• Risk Management
• Insurance
• +۶ مورد دیگر

Dr. Edward Furman is a Professor in the Department of Mathematics and Statistics at York University, leading the Actuarial Science Program and serving as Founding Director of the Risk and Insurance Studies Centre (RISC). He holds a Master's (Distinction) and PhD (Summa Cum Laude) in Actuarial Science and Probability Theory from the University of Haifa, Israel. His research focuses on distribution theory, risk measurement, insurance pricing, and systemic risk modeling. Notable achievements include winning the Fortis Chair Best Paper Prize (K. U. Leuven) for collaborative work and serving as an external consultant for institutions like the Central Bureau of Statistics (Israel) and the Society of Actuaries (U.S.). Dr. Furman's work bridges theoretical and applied domains, with funded research by NSERC, the Society of Actuaries, and the Casualty Actuarial Society. He is a Fellow of the Science Leadership Program at the University of Toronto and an Associate Editor of the Journal of Statistical Distributions and Applications (Springer). His interdisciplinary RISC unit explores holistic approaches to insurance risk, integrating actuarial science with statistical and economic frameworks. Research Themes: Dependence modeling, risk capital allocation, systemic risk measurement, and inclusive insurance design. Consulting Expertise: Collaboration with public and private sector entities on actuarial and statistical challenges. Grants: Supported by NSERC, SOA, and CAS for projects on risk aggregation and insurance pricing models.

Rafal Kulikمشاهده پروفایل

استاد

• Extreme Value Theory
• Time Series Analysis
• Long Memory and Heavy-Tailed Processes
• +۴ مورد دیگر

Rafal Kulik is a Full Professor in the Department of Mathematics and Statistics at the University of Ottawa (Faculty of Science). He holds a MSc from Ulm University (Germany) and a PhD from Wrocław University (Poland). Research Interests: Dr. Kulik specializes in Extreme Value Theory, Time Series Analysis (particularly long-range dependence and heavy-tailed processes), Data Privacy (including Differential Privacy), and Statistical Inference. His work bridges theory with applications in econometrics, financial mathematics, risk management, and privacy-preserving data analysis. Recent Contributions: Recent research focuses on clustering of extremes in stochastic processes, statistical foundations of differential privacy, and high-dimensional data analysis. Key methods include bootstrap techniques for tail processes, asymptotic expansions for block estimators, and analysis of regularly varying time series. Advising & Grants: Supervised over a dozen PhD/MSc students and postdocs. Current projects include NSERC grants on Extreme Value Theory and MITACS collaborations on privacy frameworks. Active in benchmarking large language models' privacy risks. Administration: Associate Editor for Extremes , Electronic Journal of Statistics , and others. Member of the University of Ottawa Board of Governors.

Martin Herdegenمشاهده پروفایل

دانشیار

• Mathematical Finance
• Probability Theory
• Stochastic Processes
• +۵ مورد دیگر

Martin Herdegen is a Reader in Financial Mathematics at the Department of Statistics, University of Warwick. He previously served as a postdoc at ETH Zürich under Johannes Muhle-Karbe and holds a PhD in Mathematics from ETH Zürich (2014) under Martin Schweizer. His research focuses on equilibrium theory, utility maximization, stochastic processes, and risk measures, with applications to financial bubbles and market microstructure. Herdegen’s academic career includes supervising multiple PhD students (e.g., Florian Gutekunst, Andreea Popescu) and postdocs (e.g., Nazem Khan). His research group explores topics like ρ-arbitrage, recursive utility, and liquidity provision under adverse conditions. He has contributed to foundational work on strict local martingales and their implications for financial markets. Publications span leading journals such as Finance and Stochastics , Mathematical Finance , and Annals of Applied Probability , addressing equilibrium models with transaction costs, optimal investment strategies, and risk measurement techniques. His work frequently integrates stochastic analysis and control theory to solve practical finance problems. Herdegen’s research also intersects with reinforcement learning applications in trading, as seen in the Mbt-gym framework for limit order book simulations. His contributions emphasize rigorous mathematical foundations while addressing real-world market frictions and liquidity dynamics.

• Financial Economics
• Systemic Risk
• Banking and Financial Regulation
• +۷ مورد دیگر

Viral V. Acharya is a Professor of Finance and the C.V. Starr Professor of Economics at New York University's Leonard N. Stern School of Business. He is affiliated with several leading research institutions, including the Centre for Economic Policy Research (CEPR), the European Corporate Governance Institute (ECGI), and the National Bureau of Economic Research (NBER). His research spans critical areas in financial economics, with a focus on systemic risk, financial regulation, banking stability, corporate governance, liquidity risk, sovereign debt, and the implications of unconventional monetary policy. His work frequently addresses real-world financial crises and regulatory challenges. The trends in his scholarly output reveal a consistent emphasis on understanding the interconnectedness between financial institutions and broader economic stability. His publications explore topics such as credit derivatives, shadow banking, securitization, market freezes, and climate-related financial risks, often combining theoretical models with empirical analysis to inform policy. Notable contributions include seminal papers on measuring systemic risk, the design of prudential regulation, the role of liquidity in asset pricing, and the real effects of sovereign debt crises. His recent work continues to address pressing issues such as banking stress in 2023 and the rise of shadow banking in China.

Bikramjit Dasمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Operations Research
• +۷ مورد دیگر

Bikramjit Das is an Associate Professor and Associate Head of Pillar (Graduate Programme) at Singapore University of Technology and Design (SUTD). He holds a PhD in Operations Research from Cornell University and prior to SUTD, was a postdoctoral researcher at ETH Zurich’s RiskLab. His research focuses on extreme events analysis using applied probability, optimization, and statistical learning, with applications in finance, telecommunications, federated learning, and climate modeling. He teaches courses in Probability, Stochastic Modeling, and Analytics, and directs the Master of Science in Technology and Design (Data Science) program. Education: PhD in Operations Research (Cornell University), B.Stat & M.Stat (Indian Statistical Institute). Research emphasizes heavy-tailed distributions, risk contagion, and network modeling. Key areas include risk analysis in financial networks, robust optimization under uncertainty, and extreme value theory. His work bridges theoretical probability and real-world applications in data science and public policy. Notable contributions include studies on asymptotic independence in high dimensions, robust newsvendor models, and inference techniques for heavy-tailed data. His articles explore topics ranging from federated learning under noise to climate modeling and congestion phenomena in sparse networks. Collaborations include visiting positions at MIT and the Karlsruhe Institute of Technology. Active in academic leadership, he has contributed to technical reports on healthcare provider choice analysis and probabilistic flood risk assessments for nuclear power plants.

• Saddlepoint-Based Bootstrap
• Time Series Modeling
• Longitudinal Modeling
• +۳ مورد دیگر

Dr. A. Alexandre Trindade is a Professor in the Department of Mathematics & Statistics at Texas Tech University. His research focuses on developing novel statistical methodologies for complex data structures, with applications spanning finance, physics, and environmental science. He teaches graduate courses in Time Series Analysis and Nonparametric Statistics, and serves as advisor for the undergraduate Actuarial Science Minor program. His primary research areas include: Saddlepoint-Based Bootstrap (SPBB) : Methods for approximate inference in complex models with intractable distributions Time Series & Volatility Modeling : Multivariate autoregressive models, GARCH variants, and asymmetric distributions Spatial & Longitudinal Analysis : State-space models for missing data and spatial regression techniques Tail Risk Quantification : Nonparametric estimation of systemic risk measures like CoVaR Nonparametric Inference : Density estimation and signal detection for high-energy physics His publications demonstrate consistent innovation in statistical theory, particularly in developing resampling-based inference and extending time series methodologies. Collaborative projects include interdisciplinary work with petroleum engineering, nuclear science, and finance. Dr. Trindade maintains active research partnerships with national laboratories and industry, including reliability studies for Boeing and medical device research. He has developed specialized software for multivariate time series analysis and maintains public repositories for statistical computing resources.