جست‌وجوی استاد و پژوهشگر

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.

Marc Paolellaمشاهده پروفایل

استاد

• Financial Econometrics
• Time Series Analysis
• Portfolio Optimization
• +۴ مورد دیگر

Marc Paolella is a Full Professor of Empirical Finance at the University of Zurich's Department of Finance. His academic journey includes roles as Associate Professor (2003–2006) and Assistant Professor (1999–2003) at Kiel University and visiting positions at the University of Geneva. He holds a Dr. sc. pol. (1998) and Habilitation (2002) from Kiel University, alongside degrees from SUNY Stony Brook and Colorado State University. His research focuses on financial econometrics, emphasizing portfolio optimization, risk prediction, and saddlepoint approximation theory. He has authored four textbooks, including works on probability theory, statistical inference, and GARCH models. Over 50 journal articles span leading venues like the Journal of Econometrics and Journal of Financial Econometrics. Paolella's contributions include developing methods like CHICAGO for portfolio risk calculation and the COMFORT model for non-Gaussian returns. He serves as Editor-in-Chief of the journal Econometrics and holds editorial roles at multiple journals. Awards include the Elected Fellowship at the IETI and recognition for impactful research. His teaching spans advanced courses in financial econometrics, probability theory, and mathematical finance. He has supervised numerous theses and leads the Quantitative Finance program at the Science ETH UZH. Research grants include Swiss National Science Foundation funding for portfolio optimization under non-elliptical distributions.

• Statistics
• Machine Learning
• Financial Econometrics
• +۵ مورد دیگر

Jennifer Chan is a Professor in the Statistics Department at the University of Sydney's Faculty of Science. She earned her PhD from the University of New South Wales in 1997 and previously lectured at the University of Hong Kong before joining her current institution in 2006. Her research integrates statistical and machine learning models with applications in finance and insurance, including volatility modeling, Bayesian methods, and neural network applications. Her interdisciplinary research focuses on: Generalized linear mixed models and multivariate volatility measures Machine learning techniques for financial risk assessment Bayesian robustness and portfolio optimization Time-series analysis of cryptocurrencies and equity markets Recent publications demonstrate strong focus on Bayesian models in finance (42% of last 15 papers), machine learning applications (33%), and actuarial science (25%), with emerging emphasis on neural networks for financial forecasting. Awards & Honors: Second prize, Natural Science Award of China's Ministry of Education (2008) National Drug Strategy Research Scholarship (1994-1996) She supervises doctoral candidates working on machine learning applications in finance and insurance. Her international collaborations include institutions in Israel, Japan, Malaysia, and the United States.

Darfiana Nurمشاهده پروفایل

مدرس ارشد

• Nonlinear time series analysis
• Bayesian inference
• Markov Chain Monte Carlo (MCMC)
• +۵ مورد دیگر

Dr Darfiana Nur is an Adjunct Senior Lecturer at the University of Western Australia (UWA) , affiliated with the School of Physics, Mathematics and Computing and the Department of Mathematics and Statistics . She has a rich academic background with experience at Flinders University and Curtin University, and a strong record in teaching and research. Education: BMath – University of Gadjahmada (UGM), Indonesia (1988) MSc (Research) – University of Western Australia (1993) PhD in Statistics – Curtin University of Technology (1999) Research Interests: Her research focuses on nonlinear time series modelling , particularly Smooth Threshold Autoregressive (STAR) and regime-switching volatility models. She applies Bayesian MCMC algorithms and Hidden Markov Models (HMMs) in genetics and bioinformatics, and explores deep learning for time series forecasting. Her work spans DNA sequence modelling , Bayesian inference , and machine learning applications in complex data environments. She has published extensively in top-tier journals and contributed to both theoretical and applied statistics. Scientific Contributions: She has authored 23 journal publications , 10 refereed conference papers , and 35 non-refereed conference papers . Her recent work includes advanced statistical modelling in GARCH, BEKK, and LASSO frameworks. Supervision: She has supervised 7 PhD students , 1 MMath(Research) , 9 Masters by Coursework , and 7 Honours students across UWA, Flinders University, and the University of Newcastle. Teaching: Her teaching portfolio includes courses in Data Science , Mathematical Statistics , Time Series Analysis , and Bayesian Inference , delivered at both undergraduate and postgraduate levels.

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

Jeroen ROMBOUTS is a Professor at ESSEC Business School (France) and holds the Full Professor position of the Accenture Strategic Business Analytics Chair since 2017. He joined ESSEC in 2013, previously serving as Associate Professor at HEC Montreal (2004–2012). His research focuses on financial econometrics, volatility modeling, and machine learning applications in financial markets. He holds a Ph.D. in Econometrics from the Catholic University of Louvain (2004) and has held visiting professorships at numerous institutions, including the University of Melbourne, Aarhus University, and Tilburg University. Education: PhD in Econometrics (2004), Catholic University of Louvain; Master's degrees in Statistics (2001), Econometrics (2000), and Economics (1999), all from the same institution. He is also a Researcher at the Finance and Insurance Lab (CREST) since 2014 and serves on editorial boards of journals like Quantitative Finance and International Journal of Forecasting . Research Interests: His work emphasizes volatility modeling, time series analysis, and applications of machine learning to forecast financial markets. Key areas include GARCH models, structural breaks, and cross-temporal forecasting for digital platforms. He has published extensively in top journals such as Journal of Econometrics and International Journal of Forecasting . Articles Overview: Recent contributions include novel methods for cross-temporal forecast reconciliation using machine learning and sparse change-point VAR models. His work bridges econometric theory with practical applications in asset pricing and risk management. Awards: Recipient of the 2024 Risk-Shift award in France. His research has been recognized for advancing methodologies in volatility modeling and financial econometrics. Advising & Grants: While no specific grants are listed, his roles as a researcher and editor highlight significant contributions to the academic community. He advises on policy and industry applications of his models through consulting roles in financial econometrics and macroeconomic forecasting. Labs & Teams: Affiliated with the Finance and Insurance Lab (CREST) and leads the Information Systems, Data Analytics, and Operations department at ESSEC. Collaborates with global institutions on projects involving high-frequency data and platform economics.

Alain Hecqمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Financial Economics
• +۵ مورد دیگر

Alain Hecq is a Full Professor in the department of QE Econometrics at the School of Business and Economics, Maastricht University. His research focuses on econometric methodologies, particularly in time series analysis, noncausal models, and financial econometrics. He has contributed significantly to the understanding of volatility dynamics, cryptocurrency markets, and inflation targeting regimes. His work often addresses policy-relevant questions in macroeconomics and financial markets. Key research interests include mixed causal-noncausal autoregressive models, volatility modeling with MARMA-GARCH frameworks, and the application of these techniques to real-world phenomena such as oil price bubbles and cryptocurrency volatility. He has also explored the credibility of central banking policies during crises, such as the Brazilian inflation-targeting regime during the pandemic. His recent work emphasizes methodological advancements in high-dimensional time series analysis, including spectral estimation, hierarchical regularizers for mixed-frequency data, and reduced-rank matrix autoregressive models. These contributions reflect a blend of theoretical rigor and practical applicability in addressing complex economic and financial problems. While no formal awards are listed, his extensive publication record and focus on cutting-edge econometric techniques underscore his scholarly impact. Advising and grant activities are not detailed in the provided information, but his research demonstrates sustained engagement with both academic and policy-oriented audiences.

Liudas Giraitisمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Quantitative Methods
• +۳ مورد دیگر

Liudas Giraitis is a Professor of Econometrics at Queen Mary University of London, affiliated with the School of Economics and Finance. He holds a PhD from Vilnius University and has extensive research experience at Heidelberg University, Boston University, and the London School of Economics. His research focuses on econometrics and quantitative methods, emphasizing long memory processes, ARCH models, semiparametric inference, and time-varying random coefficient models. He has published extensively in leading statistical and econometric journals. His work bridges econometrics, statistics, and probability theory, with a strong emphasis on time series analysis and asymptotic theory. Recent research explores forecasting under ongoing structural change, heteroscedasticity testing, and dynamic stochastic general equilibrium (DSGE) models with financial frictions. Education: PhD in Econometrics from Vilnius University Key Research Themes: Long memory processes, ARCH/GARCH models, time-varying models, nonstationary time series, and asymptotic theory Affiliations: Queen Mary University of London, School of Economics and Finance Research Interests: Dr. Giraitis’s work addresses theoretical and applied challenges in econometric modeling, including robust inference for time series with general heterogeneity, cyclical time series analysis, and the development of asymptotic frameworks for dependent data. His contributions span both foundational theory and practical applications in finance and macroeconomics. Publications: Over 50 peer-reviewed articles in journals such as Journal of Econometrics , Annals of Statistics , and Journal of Time Series Analysis . Key works include the monograph Large Sample Inference for Long Memory Processes (2012) and recent studies on time-varying volatility and structural change. Lab/Team: Active in the School of Economics and Finance’s research group on econometric theory and applied time series analysis.

• Financial Econometrics
• Macroeconomic Forecasting
• Stochastic Volatility Models
• +۳ مورد دیگر

Maria Helena Lopes Moreira da Veiga is a Full Professor at Universidad Carlos III de Madrid, affiliated with the Macroeconomic and Financial Prediction and Analysis Research Group within the Statistics Department. Her work bridges economics, statistics, and financial engineering with focus on volatility modeling and macroeconomic forecasting. Principal investigator for geopolitical risk and financial contagion projects (2024-2026, 2019-2020) Key research areas: stochastic volatility models, energy markets, Bayesian methods, outlier detection Collaborates with institutions across Denmark, Portugal, France, Spain, and Canada Her publications demonstrate expertise in financial econometrics, particularly asymmetric volatility models and their applications in energy and stock markets. She employs advanced computational techniques like data cloning and wavelet analysis to address model uncertainty and risk measurement. Current projects focus on climate risk indices, AI-driven economic valuation, and high-dimensional time series prediction. She has received continuous funding from national agencies for uncertainty modeling in macroeconomic and financial contexts since 2006.

Richard A. Davisمشاهده پروفایل

استاد

• Applied Probability
• Time Series Analysis
• Stochastic Processes
• +۴ مورد دیگر

Richard A. Davis is a Professor of Statistics at Columbia University and a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS). He holds the Howard Levene Professorship in Statistics. His research focuses on applied probability, time series analysis, stochastic processes, and extreme value theory. Davis has contributed to nonlinear time series models, spatial modeling, and financial econometrics. He has held academic positions at Colorado State University and MIT, and has been recognized with awards including the Koopmans Prize, IBM Faculty Award, and fellowships from the Institute of Mathematical Statistics and American Statistical Association. His education includes a B.A. and Ph.D. in Mathematics from the University of California, San Diego. He co-authored influential textbooks like Time Series: Theory and Methods and has advised numerous papers on topics ranging from count time series to spatial extremes. Current research explores machine learning applications in time series and environmental risk modeling through collaborations like the STARMAP program.

• 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.

• Quantitative Finance
• Portfolio Optimization
• Covariance Matrix Estimation
• +۳ مورد دیگر

Gianluca De Nard serves as a Senior Research Associate at the University of Zurich's Department of Economics, Research Fellow at New York University's Volatility and Risk Institute, and Head of Quantitative Research at OLZ AG in Zurich. His career bridges academic research and practical finance applications, with significant contributions across multiple institutions. Dr. De Nard specializes in quantitative finance with expertise in portfolio optimization, covariance matrix estimation, and financial econometrics. His research focuses on developing robust statistical methods for handling large-dimensional financial data, with particular emphasis on shrinkage techniques and factor models. His work addresses fundamental challenges in modern portfolio theory, including nonstandard errors in financial research, climate risk integration, and AI applications in investment management. Analysis of his recent publications reveals a clear evolution toward integrating machine learning with traditional financial econometrics. His 2024-2025 work shows increasing sophistication in applying AI techniques to portfolio construction while maintaining rigorous statistical foundations. The recurring themes across his research include improving covariance matrix estimation, developing factor models for portfolio selection, and addressing methodological challenges in financial research. Dr. De Nard has established collaborations with leading researchers including Nobel laureate Robert F. Engle, Olivier Ledoit, and Michael Wolf. His work appears in top finance journals including the Journal of Finance, Journal of Financial Econometrics, and Journal of Banking and Finance, with his 2024 paper 'Nonstandard Errors' accumulating over 17,000 downloads. At OLZ AG, he applies his academic research to practical investment challenges as Head of Quantitative Research, demonstrating his ability to translate theoretical advances into real-world solutions. His dual academic-industry roles position him at the forefront of quantitative finance research and application.

Yongmiao Hongمشاهده پروفایل

استاد

• Model Specification Testing
• Nonlinear Time Series Analysis
• Financial Econometrics
• +۴ مورد دیگر

Yongmiao Hong serves as the Ernest S. Liu Professor of Economics and International Studies in the Department of Economics at Cornell University. He holds dual appointments as Professor of Statistics and field member in both the Department of Statistical Sciences and Center for Applied Mathematics. Professor Hong's research spans model specification testing, nonlinear time series analysis, financial econometrics, and empirical studies of Chinese economic systems. His methodological innovations include generalized spectral analysis for capturing nonlinear dependencies, semiparametric specification tests using orthogonal series/kernel methods, and autoregressive conditional interval (ACI) models for interval-valued time series data. His work demonstrates how interval data (e.g., daily stock price ranges) provides superior econometric estimation compared to point-valued observations. His publication record reveals consistent contributions to top-tier journals including Econometrica , Annals of Statistics , and Review of Financial Studies . Key trends show evolutionary progression from foundational specification testing (1990s) to sophisticated nonlinear time series tools (2000s), with recent focus on interval-valued modeling and multivariate conditional distribution validation. His research consistently bridges theoretical econometrics with financial market applications. Professor Hong advises doctoral students in economics and statistics, though specific advisee names aren't publicly listed. His research has been supported by grants enabling extensive empirical work on Chinese financial markets and continuous-time model validation. He previously served as President of the Chinese Economists Society in North America (2009-2010). His laboratory work centers on time series methodology development, particularly spectral analysis extensions and interval data modeling. Current projects involve refining ACI models for crude oil price forecasting and developing wavelet-based covariance matrix estimators robust to heteroskedasticity.

Chris Stewartمشاهده پروفایل

مدرس ارشد

• Applied Econometrics
• Time Series Analysis
• Banking Efficiency
• +۵ مورد دیگر

Dr. Chris Stewart is a Senior Lecturer in Economics at Kingston University's Department of Economics within the School of Law, Social and Behavioural Sciences. He joined Kingston University in February 2013 after previously teaching at London Metropolitan University and London Guildhall University. His primary academic responsibilities include teaching econometrics and microeconomics courses across undergraduate programs including Business Economics BSc, Economics BSc, and Financial Economics BSc. Stewart's research focuses on applied econometrics with particular expertise in time series analysis, bank efficiency, purchasing power parity, and foreign direct investment determinants. His work demonstrates methodological innovation in unit root testing, cointegration analysis, and financial econometrics. Recent research projects include generalizing the ARDL bounds test for I(0) variables, analyzing bank profitability determinants, and re-evaluating purchasing power parity testing methodologies. His publication record includes over 40 refereed journal articles in high-impact economics journals such as Applied Economics, Economic Modelling, Empirica, Journal of International Money and Finance, and Socio-Economic Review. His research has been submitted to multiple Research Excellence Framework (REF) assessments at Kingston University and previous institutions. Stewart serves as an assessor for internal REF mock exercises at Kingston University. Fellow of the Higher Education Academy (FHEA), 2024 Stewart's administrative roles include serving as Assessment Officer and Level 6 tutor in the Economics Department, coordinating external examiner moderation and reviewing draft assessments. His research collaborations span international institutions with co-authors from universities worldwide. He maintains active profiles on academic networking platforms including Google Scholar, ResearchGate, and Academia.edu to disseminate his research findings.

• Constrained Statistical Inference
• GARCH Models
• Extreme Value Theory
• +۴ مورد دیگر

Mervyn J. Silvapulle is a Professor in the Department of Econometrics and Business Statistics at Monash University. He holds a B.Sc.(Honours) in Mathematics and a Ph.D. in Statistics from the Australian National University (ANU), awarded in 1982. His research focuses on econometric and statistical methodology, with current interests in constrained statistical inference, GARCH models, and extreme value theory. Past research includes robust statistics, generalized linear models, ridge regression, and minimum distance methods of inference. Education: B.Sc. (Honours) in Mathematics, Ph.D. in Statistics (ANU). Editorial Roles: Editor (Theory and Methods) of the Australian and New Zealand Journal of Statistics, guest editor for special issues on constrained statistical inference, and associate editor for multiple journals, including the Journal of Statistical Planning and Inference and Sankhya. His research spans collaborations with institutions like the Australian Research Council (ARC), Universität St. Gallen, and the European Centre for Advanced Research in Economics and Statistics. He has led or co-investigated projects such as 'Robust methods for heteroscedastic regression models for time series' (2015–2022) and 'Non-parametric estimation of forecast distributions in non-Gaussian state space models' (2009–2012). Grants & Advising: Accepting PhD students since 1995, with a history of securing ARC-funded projects.

Belen Martin-Barraganمشاهده پروفایل

پژوهشگر

• Machine Learning
• Mathematical Programming
• Operational Research
• +۶ مورد دیگر

Belen Martin-Barragan is a Reader in Management Science at the University of Edinburgh Business School , with a focus on the Department of Management Science and Business Economics . Her research bridges Machine Learning and Mathematical Programming , emphasizing Explainable Artificial Intelligence (XAI) and applications in Operational Research , including classification , clustering , inventory management , and routing optimization . Her work includes developing interpretable machine learning models for credit scoring, healthcare data, and sustainable logistics. She has led EPSRC-funded projects on Optimisation Models for Interpretable Analytics and contributed to journals like European Journal of Operational Research , Risk Analysis , and Computers and Operations Research . Key methodologies involve Mixed-Integer Linear Programming , Support Vector Machines , and stochastic dynamic programming . Her research fingerprint spans Machine Learning (97%), Mathematical Programming (75%), and Optimization Algorithms (20%). She has been a Research Champion and Deputy Director of Research (Ethics and Integrity) at the Business School, with affiliations to the Credit Research Centre and Edinburgh Strategic Resilience Initiative .