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Paolo MAZZA is a Full Professor of Finance at IÉSEG School of Management in France, specializing in Management Sciences and Finance. He holds a HDR (Habilitation à Diriger des Recherches) from the University of Paris Dauphine and a Ph.D. in Finance from the Louvain School of Management. His research focuses on econometrics, quantitative methods, market liquidity, and corporate finance. He has advised on finance and audit-related matters and is a member of the LEM research group. His work spans topics such as insider trading performance, carbon market integration, cryptocurrency dynamics, and technical analysis strategies. Education: 2017: HDR in Management Sciences (Finance), University of Paris Dauphine 2013: Ph.D. in Finance, Louvain School of Management 2008: Master's in Management Sciences (Finance), UCLouvain Research Interests: Prof. Mazza’s research emphasizes quantitative finance, market microstructure, and regulatory frameworks. He explores how technical analysis impacts market quality, evaluates the performance of insider trading in emerging markets, and investigates carbon market dependencies. His work often combines econometric models with real-world market data to address practical financial questions. Key Contributions: His recent studies include analyzing corporate legal insider trading performance in Vietnam, Korea, and China, and assessing the integration of global carbon markets using GARCH-copula models. He has also contributed to understanding cryptocurrency volatility during market stress periods and refining fund rating methodologies using nonparametric frontier models. Awards: Best Dissertation Award (2008), Facultés Universitaires Catholiques de Mons Teaching & Professional Experience: Prof. Mazza teaches technical analysis, financial markets, and quantitative methods at the undergraduate and graduate levels. Prior to academia, he worked as a Performance Analyst at Dexia Asset Management and as a researcher at the Louvain School of Management.

• Finance
• Econometrics
• Quantitative Methods
• +7 more

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.

• Financial Asset Valuation
• Portfolio Management
• Risk Measurement and Management
• +5 more

Dr. Shyamal Ghosh is an Assistant Professor Grade I in Data Science at the Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM). Previously, he served as an Assistant Professor at the Indian Institute of Information Technology Guwahati (2021-2023), a Post-Doctoral Fellow at the University of the Free State, South Africa (2020-2021), and a Visiting Scientist at the Indian Statistical Institute, Kolkata (2019-2020). His educational background includes: M.Sc. in Applied Mathematics from Indian Institute of Engineering Science and Technology, Shibpur (2019), with thesis on "On Certain Probabilistic and Inferential Issues in Nonparametric Ageing Models". B.Sc. (Hons.) in Mathematics from Indian Institute of Engineering Science and Technology, Shibpur (2014). Studies at Vidyasagar University (2009-2012). Dr. Ghosh's research focuses on Dependence Modelling using Copula , Stochastic Degradation Modelling , Statistical Inference , Stochastic Ordering , Shock Model Theory , and Mathematical Theory of Reliability . His work bridges theoretical statistics with applications in reliability engineering, environmental science, and computer systems. He has developed novel statistical tests for ageing classes and methods for stochastic comparisons in heterogeneous systems. His recent publications (2020-2025) span journals in statistics, reliability engineering, and computer science, showing a trend towards interdisciplinary applications. Key areas include nonparametric testing for reliability classes, copula-based dependence modeling, and resource optimization in fog computing. His work often involves moment inequalities, stochastic orders, and lifetime distributions. Dr. Ghosh is actively seeking PhD students with fellowships (e.g., CSIR-JRF, INSPIRE) in Applied Probability and Statistics. He has taught courses such as Statistical Modeling, Survival Analysis, and Mathematical Statistics at IISER TVM and IIIT Guwahati.

• Dependence Modelling using Copula
• Stochastic Degradation Modelling
• Statistical Inference
• +3 more