Valentina
Raponi
Associate Professor of Financial Management
• Ph.D. in Finance, Imperial College Business School
• Ph.D. in Methodological Statistics, University of Rome La Sapienza
• M.Sc. in Econometrics and Mathematical Economics, London School of Economics and Political Science
• M.Sc. in Statistics and Economics, University of Rome La Sapienza
• B.Sc. in Statistics and Economics, University of Rome La Sapienza
Valentina Raponi is an Associate Professor in the Financial Management Department. Valentina Raponi is a researcher in financial econometrics and empirical asset pricing. Her work studies how risk is measured and priced in financial markets, with particular emphasis on asset pricing models, portfolio allocation, and the role of information in financial markets.
Her research combines econometric methods with large financial datasets to investigate questions related to market efficiency, factor models, and the statistical properties of asset returns. Her recent work examines the role of anomalies in asset pricing, the implications of weak factors for empirical asset pricing models and portfolio strategies, the information loss caused by portfolio aggregation, and how new information technologies affect the way financial markets process public information.
At IESE, Valentina teaches courses in Operational Finance and Corporate Finance at the MBA and Executive MBA levels, as well as advanced courses in Econometrics at the Master’s and PhD levels.
Valentina holds a PhD in Finance from Imperial College Business School and a PhD in Methodological Statistics from Sapienza University of Rome. She previously studied econometrics and statistical economics at the London School of Economics and Sapienza University of Rome.
Before joining IESE Business School, Valentina also worked as an intern at the European Central Bank, the Bank of Italy and the Italian Ministry of Economy and Finance.
Areas of interest.
• Financial Econometrics
• Empirical asset pricing
• Asset pricing anomalies
• Portfolio Choice
• Econometric Theory
• Machine Learning